Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling

Community preventive behaviour and perception on... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/12-966" }, "headline": "Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022:...", "datePublished": "2023-08-10T15:06:36", "dateModified": "2024-10-03T16:59:37", "author": [ { "@type": "Person", "name": "Tris Eryando" }, { "@type": "Person", "name": "Tiopan Sipahutar" }, { "@type": "Person", "name": "Sandeep Poddar" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background This study investigated the determinants of community preventive behavior in complying with the Indonesian regulations to prevent COVID-19 local transmission. Methods A cross-sectional study used to collect the data via an online cross using a form created from a google questionnaire forms. A total of 1,802 respondents were gathered at a single point in time. The authors used the Health Belief Model (HBM) approach to measure and create a model for the prevention of local transmission of COVID-19. Results This study found that more than half of the respondents still had low perceived susceptibility (16%) and severity (43%). There were only 3% respondents with perceived barriers and 19% with strong self-efficacy. The findings showed that self-efficacy and perceived barriers had statistically significant relationships with preventive behavior (p-value <0.05). The goodness of fit index showed that the proposed model was not fit for the data (RMSE0.950, AGFI>0.950, SRMR<0.100), which means that it was not fit to describe the empirical phenomenon under study. Conclusions This study found that more than half of the respondents still had low perceived susceptibility (84%) and severity (67%), but more than half had high perceived benefits (54%). Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviours (3%). Still, most respondents had low perceived self-efficacy (81%), and only 60% had good behaviours related to COVID-19 prevention. In the context of COVID-19 preventive behaviour, we recommended to improve perceived susceptibility and severity by providing the correct information (which contain information about how people susceptible to the virus and the impact of infected by the virus) with the local cultural context. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/12-966/v3", "name": "Community preventive behaviour and perception on the severity of COVID-19..." } } ] } Home Browse Community preventive behaviour and perception on the severity of COVID-19... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Eryando T, Sipahutar T and Poddar S. Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.12688/f1000research.135262.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] Tris Eryando https://orcid.org/0000-0001-9053-3174 1 , Tiopan Sipahutar https://orcid.org/0000-0002-5292-1261 1 , Sandeep Poddar https://orcid.org/0000-0001-9771-877X 2 Tris Eryando https://orcid.org/0000-0001-9053-3174 1 , Tiopan Sipahutar https://orcid.org/0000-0002-5292-1261 1 , Sandeep Poddar https://orcid.org/0000-0001-9771-877X 2 PUBLISHED 03 Oct 2024 Author details Author details 1 Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, West Java, 16424, Indonesia 2 Research and Innovations, Lincoln University College,, Petaling Jaya, Selangor, 47301, Malaysia Tris Eryando Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Review & Editing Tiopan Sipahutar Roles: Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation Sandeep Poddar Roles: Visualization, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Emerging Diseases and Outbreaks gateway. This article is included in the Coronavirus (COVID-19) collection. Abstract Background This study investigated the determinants of community preventive behavior in complying with the Indonesian regulations to prevent COVID-19 local transmission. Methods A cross-sectional study used to collect the data via an online cross using a form created from a google questionnaire forms. A total of 1,802 respondents were gathered at a single point in time. The authors used the Health Belief Model (HBM) approach to measure and create a model for the prevention of local transmission of COVID-19. Results This study found that more than half of the respondents still had low perceived susceptibility (16%) and severity (43%). There were only 3% respondents with perceived barriers and 19% with strong self-efficacy. The findings showed that self-efficacy and perceived barriers had statistically significant relationships with preventive behavior (p-value <0.05). The goodness of fit index showed that the proposed model was not fit for the data (RMSE0.950, AGFI>0.950, SRMR<0.100), which means that it was not fit to describe the empirical phenomenon under study. Conclusions This study found that more than half of the respondents still had low perceived susceptibility (84%) and severity (67%), but more than half had high perceived benefits (54%). Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviours (3%). Still, most respondents had low perceived self-efficacy (81%), and only 60% had good behaviours related to COVID-19 prevention. In the context of COVID-19 preventive behaviour, we recommended to improve perceived susceptibility and severity by providing the correct information (which contain information about how people susceptible to the virus and the impact of infected by the virus) with the local cultural context. READ ALL READ LESS Keywords COVID-19; perceive; preventive behavior; Health Belief Model; Indonesia Corresponding Author(s) Tris Eryando ( [email protected] ) Close Corresponding author: Tris Eryando Competing interests: No competing interests were disclosed. Grant information: This research was funded by Universitas Indonesia under contract number NKB-630/UN2.RST/HKP.05.00/2022 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2024 Eryando T et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Eryando T, Sipahutar T and Poddar S. Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.12688/f1000research.135262.3 ) First published: 10 Aug 2023, 12 :966 ( https://doi.org/10.12688/f1000research.135262.1 ) Latest published: 03 Oct 2024, 12 :966 ( https://doi.org/10.12688/f1000research.135262.3 ) Revised Amendments from Version 2 There are no major difference between the previous version with the latest. We added a statement in part of HBM conceptual, we added one reference in discussion as suggested, and we added statement on data collection limitation. As additional, as we reviewed the references, we also added several incomplete references (which was marked with blue text before). There are no major difference between the previous version with the latest. We added a statement in part of HBM conceptual, we added one reference in discussion as suggested, and we added statement on data collection limitation. As additional, as we reviewed the references, we also added several incomplete references (which was marked with blue text before). See the authors' detailed response to the review by Valentina Lucia La Rosa See the authors' detailed response to the review by Rotimi Oguntayo READ REVIEWER RESPONSES Introduction The world is currently besieged by the COVID-19 pandemic. 1 As of February 7, 2022, there were 394,381,395 confirmed cases of COVID-19, with the World Health Organization (WHO) reporting 5,735,179 deaths. 2 The COVID-19 pandemic in Indonesia from mid-2021 until the end of 2021 reached its peak (second wave) when it was dominated by the Delta variant with the total confirmed cases was 4.254.443 and 143.766 death (CFR: 3.4). 3 COVID-19 prevention regulations largely depend on community compliance and behavior. While changing behavior is a significant challenge in health interventions. An AC Nielsen survey (2020) in six major cities with a total of 2,000 respondents, in collaboration with the United Nations International Children’s Emergency Fund (UNICEF), found that less than one-third (31.5%) of all respondents practiced all three-preventive activities (wearing masks, washing hands, and social distancing). Over one-third (36%) practiced only two of the three-preventive behaviors, less than one-fourth (23.2%) practiced only one of the three behaviors, and almost one-tenth (9.3%) did nothing at all. 3 A number of studies described the associations between socio-demographic characteristics and people’s levels of perceptions about the severity of COVID-19 with their preventive behavior. 3 – 10 Based on the official government report (see https://covid19.go.id/monitoring-kepatuhan-protokol-kesehatan ), some areas of Indonesia showed a compliance rate of only 60% till I January 2022. 11 The aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. The following research questions were formulated: 1) What constitutes as the community perceptions of COVID-19? 2) Is there an association between community perception and community preventive behaviour using the Health Belief Model (HBM) approach? And 3) what kind of model of preventive behaviour on COVID-19 can be predicted using the structural equation model (SEM)? Methods Study design and samples This was a cross-sectional study using Google forms with structured survey questionnaires. The questionnaires have been tested with 30 respondents before the survey was conducted. The proportion of a large population was used to figure out the sample size for a variable, which was used to figure out the minimum sample size. 12 The margin error=5%, p=50%, and Zα=95%. The minimum sample size for this study was 385 respondents. The data collection was conducted over two weeks from July 14–26, 2021, using WhatsApp, Line, and Telegram. To expand the coverage throughout Indonesia, social media influencers were asked to distribute the survey through Twitter and Instagram. The respondents who participated in this study were aged 15–62 and Indonesian citizens. On average, it took 20–25 minutes to complete the form. Respondents signed informed consent forms before completing the survey. The total final number of respondents was 1,802. Conceptual model We proposed a comprehensive approach to understanding behaviour change using the Health Belief Model rather than study it partially. The HBM offers a holistic perspective on behaviour change processes. However, when employing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. The HBM considers several main concepts/theories that predict why people will take action to prevent, including individual characteristics, perceived susceptibility, severity, benefit, and self-efficacy. 13 Since this study aims to observe the behavior change especially preventive health behavior on COVID-19, the HBM conceptual is one of the complete and holistic frameworks that can be used to figure out how a behavior occurs and change. Theories and models serve the purpose of explaining behavior and offering strategies for promoting behavioral changes. An explanatory theory, serves to elucidate and understand the root causes of a problem. These theories also forecast behaviors in specific circumstances and assist in identifying factors that can be altered, such as knowledge, attitudes, self-efficacy, social support, and resource availability. Theories and models can be applied to investigate the reasons behind people not adhering to public health or medical advice and neglecting their health. They can aid in pinpointing the essential information required before developing and structuring an intervention program. 14 Individual characteristics and community perception of COVID-19 Individual characteristics were represented by residence (R), age (A), gender (G), educational level (E), and occupation (O). Perceived susceptibility, severity, benefit, barriers, and self-efficacy are latent variables in this study. All questions in in the questionnaire in each latent variable were answered using a five-point Likert scale: 1 (strongly disagree), 2 (disagree), 3 (neutral), 4 (agree), and 5 (strongly agree). Observed variables X1-14, S1-5, BEN1-5, BAR1-5, and SE1-5 were used to measure perceived susceptibility, severity, benefit, barriers, and self-efficacy, in that order. Community preventive behavior against COVID-19 transmission Community preventive behavior was represented by six indicators in Figure 1 which include frequency of hosting (BHV1), frequency of visiting others (BHV2), frequency of work/study from office/school (BHV3), frequency of handshaking (BHV4), frequency travelling to a red zone (BHV5), and frequency of leaving the house when you are not feeling well (BHV6). Figure 1. Proposed structural model of community preventive behavior against COVID-19. 15 Data analysis This study is a kind of behavioral sciences that intended to study theoretical construct which is cannot be observed directly- called as latent variables. Thus, we operationally define the latent variable of interest in terms of behavior believed to represent it. In this study, the latent variable includes perceived susceptibility, perceived severity, perceived benefit, perceived barriers, and perceived self-efficacy and for each latent variables, there are observed variables which is based on theory are representing each of latent variables. SEM analysis allowed us to not only analyze observed measurement variable, but it can incorporate both unobserved (latent) and observed variables. One of the specialties of SEM is the hypothesized model can then be tested statistically in a simultaneous analysis of the entire system of variables to determine the extent to which it is consistent with the data. If goodness-of-fit is adequate, the model argues for the plausibility of postulated relations among variables; if it is inadequate, the tenability of such relations is rejected. 16 The sample size required using SEM analysis in this study is sufficient (n=1802). The minimum sample size for SEM analysis with seven or less constructs and no under identified constructs is 150. 17 The authors used Lisrel version 8.8 software to construct the covariance-based SEM. SEM analysis can also be done using R. Steps of doing SEM analysis using R as follows 18 : a. Draw model b. Input data in the form of covariance or correlation matrix c. Identify the model d. Assess parameter estimates e. Assess fit measure (chi-square, degree of freedom, residual matrix, GFI, RMSEA) f. Check the modification indices g. Rerun the model till we get the best fit of the data to the model ad theory. The six latent variables were perceived susceptibility, severity, benefit, barriers, self-efficacy, and preventive behavior. The 45 observed variables were presented in Table 1 include construct that build by all the observed variables. Table 1. The observed variables. No Item Scale Description Construct 1 R 1. City 2. Village 3. Housing area 4. Apartment Residence - 2 A Age - 3 G 1. Male 2. Female Gender - 4 E 1. No education 2. Elementary school 3. Junior high school 4. Senior high school 5. University Education level - 5 O 1. Unemployment 2. Labourer/employee 3. Student/college 4. Housewife 5. Entrepreneur 6. Pensionary 7. Others Occupation - 6 X1 1-5 Likert strongly disagree – strongly agree I am at risk when not keeping a distance of at least 1-2 metres when in the work area. Perceive Susceptibility 7 X2 I am at risk when not keeping a distance of at least 1-2 metres when at school. 8 X3 I am at risk of not keeping a distance of at least 1-2 metres when at the mall/supermarket. 9 X4 I am at risk when not keeping a distance of at least 1-2 metres when at the traditional market. 10 X5 I am at risk when not keeping a distance of at least 1-2 metres at a restaurant/coffee shop/café. 11 X6 I am at risk when not keeping a distance of at least 1-2 metres when at a touristdestination. 12 X7 I am at risk when not keeping a distance of at least 1-2 metres when at a wedding reception. 13 X8 I have a low risk of getting COVID-19 if I avoid using a bus/taxi. 14 X9 I have a low risk of getting COVID-19 if I avoid using a commuter line. 15 X10 I have a low risk of getting COVID-19 if I avoid using a small bus. 16 X11 I have a low risk of getting COVID-19 if I avoid using motorcycle taxis/online. 17 X12 I have a low risk of getting COVID-19 if I practice work/study from home. 18 X13 I have a low risk of getting COVID-19 if I practice avoiding handshaking. 19 X14 I have a low risk of getting COVID-19 if I practice avoiding travel to a red zone (an area with a high positive rate). 20 S1 I may get COVID-19 and cause health impacts if I do not keep a distance ofat least 1-2 metresina public area. Perceived severity 21 S2 I may get COVID-19 and cause health impacts if I do not avoid using public transportation. 22 S3 I may get COVID-19 and cause health impacts if I do not work/study from home. 23 S4 I may get COVID-19 and cause health impacts if I do not avoid handshaking. 24 S5 I may get COVID-19 and cause health impacts if I do not avoid travel to a red zone. 25 BEN1 I feel safe from COVID-19 if I keep a distance of at least 1-2 metresin a public area. Perceived benefit 26 BEN2 I feel safe from COVID-19 if I avoid using public transportation. 27 BEN3 I feel safe from COVID-19 if I work/study from home. 28 BEN4 I feel safe from COVID-19 if I avoid handshaking. 29 BEN5 I feel safe from COVID-19 if I avoid travel to a red zone. 30 BAR1 I find it difficult to stay at least 1-2 metres from people in a public area. Perceived barrier 31 BAR2 I find it difficult to avoid using public transportation. 32 BAR3 I find it difficult to work/study from home. 33 BAR4 I find it difficult to avoid handshaking. 34 BAR5 I find it difficult to avoid travel to a red zone. 35 SE1 It is easy for me to avoid travel to a red zone. Perceived self-efficacy 36 SE2 It is easy for me to avoid handshaking. 37 SE3 It is easy for me to work/study from home. 38 SE4 It is easy for me to avoid using public transportation. 39 SE5 It is easy for me to keep a distance of at least 1-2 metres when in a public area. 40 BHV1 Frequency of hosting. Behaviour 41 BHV2 Frequency of visiting others. 42 BHV3 Frequency of work/study from office/school. 43 BHV4 Frequency of handshaking. 44 BHV5 Frequency travelling to a red zone. 45 BHV6 Frequency of leaving the house when you are not feeling well. Descriptive statistics were presented as numbers and percentages for individual characteristics, and bar charts for perceptions and behavior. Descriptive statistical analysis was performed using IBM Statistical Package for the Social Sciences (SPSS) version 27 and presented using Microsoft Excel. The descriptive analysis simply can also be completed using Microsoft Excel. The SEM analysis was carried out using the following steps: 1) Identifying the degree of freedom of the structural model. 2) Assessing construct validity. 3) Assessing construct reliability. 4) Assessing structural model validity. 14 , 19 Perceived was categorized based on the total score. Good/high perceived were those who chose to “strongly agree” on every question. Regarding behavior, good behavior occurs if the respondent states never or rarely. Rarely was included in the category based on the assumption that people might be challenged to practice preventive behavior related to other factors that require them to leave, such as the environment or critical social activities that cannot be abandoned. Results The study results are presented in several parts sequentially, starting with the respondent’s characteristics, followed by the descriptive statistics of the independent variable (community perception of the level of severity of COVID-19 disease), the descriptive statistics of the dependent variable (composite variable of community behavior), and the SEM analysis results. Most of the respondents lived in the city (46.1%) and a housing area (36.1%), and only a few lived-in villages (16.6%). A large majority of the respondents were women (74.5%), and approximately 80.0% were students and workers ( Table 2 ). Table 2. Respondents’ characteristics. Frequency % Resident City 831 46.1 Village 299 16.6 Housing area 651 36.1 Apartment 21 1.2 Sex Male 460 25.5 Female 1342 74.5 Education Level No education 3 0.2 Elementary school 3 0,2 Junior high school 25 1.4 Senior high school 885 49.1 University 886 49.2 Occupation Unemployment 128 7.1 Laborer/employee 552 30.6 Student/college 958 53.2 Housewife 35 1.9 Entrepreneur 106 5.9 Pensionary 3 0.2 Others 20 1.1 In terms of perceived susceptibility ( Table 3 ), more than half of respondents (>50%) chose to strongly agree to practice recommended behaviors such as social distancing at least 1-2 meters in public areas; almost half of them strongly agree that they had a low risk of getting COVID-19 when avoiding using public transportation, practicing work/study from home, handshaking, and travelling to a red zone. Most respondents also strongly agree that if they are exposed to COVID-19, it will affect their health if they do not practice recommended behaviors (perceived severity - Table 3 ). Likewise, concerning perceived benefit ( Table 3 ), more than 50% of respondents strongly agree that they can avoid getting COVID-19 if they practice these behaviors. In terms of perceived barriers, less than 20% felt that it would be challenging/difficult to implement the recommended behaviors ( Table 3 ). Regarding self-efficacy, this is defined as the conviction that one can successfully practice a certain behavior, 13 the survey showed that less than 50% were confident that they could implement the recommended behavior ( Table 3 ). Table 3. Distribution (%) of perceived items (N=1802). Perceived 1 (%) 2 (%) 3 (%) 4 (%) 5 (%) Perceived Susceptibility I am at high risk when I am not keeping a distance of at least 1-2 m when in a public area Working area 3.5 2.1 6.3 27 61.2 School 3.3 2.6 8.0 28 58.1 Mall/supermarket 3.1 1.7 5.7 22.6 67 Traditional market 2.9 1.6 5.5 20.5 69.5 Restaurant/coffee shop/café 2.7 2.6 7.5 27.1 60 Tourism objects 3.4 3.2 10.5 27.1 55.8 Wedding reception 2.9 1.8 6.4 22.1 66.8 I have a low risk of getting COVID-19 if I avoid using public transportation Bus/taxi 6 6 11.4 25.9 50.8 Commuter line 6.4 5.6 12.2 23.9 51.9 City transport 6.4 5.1 9.5 23.5 55.5 Offline motorcycle taxi/online 5.1 10.8 28.4 30.7 25 I have a low risk of getting COVID-19 if I practice following behaviours Work/study from home 3.4 0.9 5.2 19.3 71.2 Avoid handshaking 2.7 0.9 3.4 19 74 Avoid travel to red zone (are with high positive rate) 2.6 1 3.3 16.6 76.5 Perceived Severity I may get COVID-19 and cause health impacts to myself if I do not follow behaviors Keep distance at least 1-2 metre when at public area 3.4 1.6 4.9 26.2 63.9 Avoid using public transportation 3.8 2.9 13.5 29.3 50.4 Work/study from home 5.0 2.4 7.3 27.9 57.4 Avoid handshaking 4.3 1.9 4.2 24.5 65.1 Avoid travel to red zone (are with high positive rate) 4.6 1.6 4.2 21.0 68.6 Perceived Benefit I feel safe and get off COVID-19 if I practice following behaviours Keep distance at least 1-2 metre when at public area 2.6 1.1 4.8 23 68.5 Avoid using public transportation 2.3 1.6 9.3 23.9 62.8 Work/study from home 2.6 1.3 5.9 22.4 67.8 Avoid handshaking 2.6 1.2 4.6 20 71.8 Avoid travel to red zone (are with high positive rate) 2.5 1.2 4.3 18.2 73.9 Perceived of Barrier I find it difficult to practice the following behaviours Keep distance at least 1-2 metre when at public area 23.3 24.4 16.4 23.6 12.4 Avoid using public transportation 34.8 25.2 15.8 14.1 10 Work/study from home 35.2 25.1 15.9 12.4 11.4 Avoid handshaking 38.8 28.9 14.2 10.2 7.9 Avoid travel to red zone (are with high positive rate) 37.0 25.6 15.9 12.2 9.3 Self-Efficacy It is easy for me to practice following behaviour Keep distance at least 1-2 metre when at public area 4.5 11.8 17.0 30 36.7 Avoid using public transportation 4.3 9.8 15.5 23.8 46.5 Work/study from home 6.3 8.0 13.0 24.6 48 Avoid handshaking 3.1 5.0 11.2 28.4 52.4 Avoid travel to red zone (are with high positive rate) 5.0 7.2 13.0 23.9 50.9 For each type of recommended behavior as mentioned in Table 1 to prevent the transmission of COVID-19, more than 50% of respondents never and rarely hosted, visited others, worked from the office/school, shook hands, travelled to a red zone, and left the house when not feeling well ( Figure 2 ). Figure 2. The description of behavior. Notes: 1: Never; 2: rarely; 3: occasionally, 4: frequently, 5: always. Based on the composite of each perceived item, it was found that only 16% of the respondents had a high percentage of good perceived susceptibility; perceived severity was 43%, perceived benefit was 54%, perceived barrier was 3%, self-efficacy was 19%, and only 60% of respondents practiced good behaviours. SEM results Identify degree of freedom of structural model The second step in SEM analysis is to run the identification of observed variables. A general requirement for identifying any type of model in SEM are the model’s degrees of freedom which must be a least zero (df M ≥ 0). Hence, the solution to meet the requirement is to identify whether the model is under identified, just identified, or overidentified. Overidentified is mandatory in order to meet the requirement. An overidentified structural equation model is identified and has more observations than free parameters (df M >0)(2). In this study, we found the degree of freedom value to be 821, hence it was concluded that the model was over-identified. Thus, the next step of the analysis can be completed. Construct validity Construct validity was performed to test whether the instrument or measurement variable could describe the latent variable correctly and precisely. For this, two tests were conducted: validity, which consisted of convergent and discriminant validity, and reliability. 15 A convergent validity test examined the loading factor value of the measurement variable in each latent variable construct. If the loading factor value was greater than 0.50, the latent variable construct had good convergent validity. 15 The results of the convergent validity test showed that almost all items in this study had a loading factor value of more than 0.5, except for items X11 and BHV3. These two items have a loading factor of ≤ 0.5, which indicates that they do not meet the criteria for convergent validity. Hair et al. (2019) stated that items with low loading factors that do not meet the limits of convergent validity should be excluded from the measurement of latent variables. Therefore, items X11 and BHV3 in this study were not included in the measurement of the latent variable. The convergent validity test was then carried out for a second time. All of the items had good convergent validity, which was shown by loading factor values of more than 0.5. The discriminant validity test was carried out by comparing the root value of each latent variable’s average variance extracted (AVE) with the correlation of these latent variables with other latent variables. If the root value of the variable AVE was greater than the correlation of the variable with other variables in the model, the indicator/question item had good discriminant validity. Table 4 shows the AVE root value for each latent variable and the correlation coefficient between the latent variables. The value of the AVE root is shown as the value on the diagonal of the matrix, while the values beside and below the AVE root are the correlation coefficients between two pairs of variables. The results of the evaluation of discriminant validity show that the root value of AVE in each latent variable is greater than the correlation coefficient of the latent variable with other latent variables in the structural model. Thus, it can be stated that the items/instruments in this study have good discriminant validity. Table 4. Results of the discriminant validity test. Suscept Severity Benefit Barrier Efficacy Behaviour Suscept 0.822 - - - - - Severity 0.730 0.911 - - - - Benefit 0.590 0.810 0.936 - - - Barrier -0.160 -0.210 -0.260 0.737 Efficacy 0.080 0.100 0.120 -0.480 0.725 Behaviour -0.120 -0.150 -0.160 0.460 -0.320 0.630 Construct reliability The construct reliability test was done by examining the composite reliability value. If the combined reliability value is greater than 0.7, it can be said that the variables in the study already have reliable indicators/question items. 19 All latent variables were found to have a composite reliability value of more than 0.7, which means that each variable has a consistent measurement indicator and good internal consistency. Structural model validity Two analyses were conducted to evaluate the structural model validity:1) dependency test and 2) assessing the goodness-of-fit of the model. Dependency test The dependence relationship test was employed by looking at the path coefficient and its p -value in the structural model. The path coefficient shows the magnitude and direction of the relationship between the two variables. According to Table 5 , sex had a statistically significant relationship with perceive susceptibility ( p -value = 0.000); perceived susceptibility had a statistically significant relationship with perceived severity ( p -value = 0.000); age ( p -value = 0.004), sex ( p -value = 0.030), and education (( p -value = 0.050) had significant relationships with perceived benefit; perceived benefit ( p -value = 0.000), resident ( p -value = 0.009), and sex ( p -value = 0.000) had a significant relationship with perceive barrier; and perceive barrier ( p -value = 0.000) and occupancy ( p -value = 0.040) had a significant relationship with perceive self-efficacy. Regarding behavior, only perceived barriers ( p -value = 0.000) and self-efficacy ( p -value = 0.000) had statistically significant relationships with COVID-19 prevention behavior. The final structural model is as follows: behaviour = - 0.018*susceptibility - 0.041*severity - 0.00045*benefit + 0.39*barrier - 0.13*self-efficacy. Table 5. Results of the SEM statistical test. Latent variable Independent Path coefficient Std. error t-Statistics p-value Perceived Susceptibility Resident 0.009 -0.010 0.870 0.273 Age 0.005 -0.005 1.020 0.237 Sex 0.180 -0.024 7.540 0.000 Education 0.005 -0.006 0.860 0.276 Occupancy 0.045 -0.029 1.520 0.126 Perceived Severity Perceived Susceptibility 0.720 -0.020 35.810 0.000 Regional 0.012 -0.007 1.690 0.096 Age 0.001 -0.003 0.280 0.384 Sex 0.014 -0.017 0.810 0.287 Education -0.008 -0.005 -1.680 0.097 Occupancy -0.020 -0.021 -0.960 0.252 Perceived Benefit Perceived Severity 0.810 -0.018 44.660 0.000 Resident -0.010 -0.006 -1.550 0.120 Age -0.009 -0.003 -3.030 0.004 Sex 0.034 -0.015 2.270 0.030 Education 0.008 -0.004 2.040 0.050 Occupancy 0.032 -0.018 1.770 0.083 Perceived Barrier Perceived Benefit -0.240 -0.026 -9.260 0.000 Resident -0.028 -0.010 -2.770 0.009 Age -0.009 -0.005 -1.940 0.061 Sex -0.130 -0.025 -5.050 0.000 Education 0.009 -0.007 1.340 0.163 Occupancy 0.051 -0.031 1.670 0.099 Perceived Self-Efficacy Perceived Barrier -0.480 -0.031 -15.860 0.000 Resident 0.008 -0.010 0.840 0.280 Age -0.003 -0.005 -0.550 0.343 Sex -0.022 -0.024 -0.940 0.256 Education 0.001 -0.006 0.081 0.398 Occupancy 0.063 -0.029 2.150 0.040 Individual Behaviour Perceived Susceptibility -0.018 -0.037 -0.500 0.352 Perceived Severity -0.041 -0.053 -0.780 0.294 Perceived Benefit 0.000 -0.045 -0.010 0.399 Perceived Barrier 0.390 -0.036 10.930 0.000 Perceived Self-Efficacy -0.130 -0.032 -4.000 0.000 The goodness-of-fit model assessment results in Table 6 . showed that none met the goodness-of-fit criteria. Therefore, the structural model in this study was not fit for the data and was not fit to describe the empirical phenomenon under study. Table 6. Goodness-of-fit index results. Goodness-of-fit index Cut of value Value Conclusion Probability of χ 2 >0.050 0.000 No fit RMSEA 0.950 0.560 No fit AGFI >0.950 0.490 No fit SRMR 0.950 0.850 No fit NFI >0.950 0.850 No fit PNFI >0.950 0.770 No fit PGFI >0.950 0.480 No fit IFI >0.950 0.850 No fit RFI >0.950 0.830 No fit Discussion The results of categorized all perceived items revealed that only a small proportion of respondents (16%) held beliefs about their chances of experiencing COVID-19 (perceived susceptibility), while 43% of respondents believed in the severity of the COVID-19 effects were on their health. More than half of the respondents believed that the recommended behaviors to prevent COVID-19 infection could protect them from getting an infection (perceived benefit). Only 3% of respondents believed some things hindered the practice of recommended behaviors (perceive barrier), and only 19% believed that they could practice the recommended behaviors (perceive self-efficacy). This study’s results are similar to those conducted in India, Sri Lanka, Iran, and Ethiopia. 8 , 10 , 17 , 20 A study in Italiy conducted to nine hundred and seventy-eight Italian adolescents found a similar pattern where they had a low perception of COVID-19 risk, as well as perceived comparative susceptibility and perceived seriousness. They think that COVID-19 is not a potentially severe disease for them as many news stated that young people are less vulnerable to the COVID-19 effect. 21 The HBM theory holds that people are likely to practice preventive behaviors or actions if: 1) They believe that it will reduce their risks, 13 2) They perceive themselves as susceptible to COVID-19 infection, 3) They believe that this condition would have a potentially serious impact, 4) They acknowledge the benefit of recommended actions/behaviours in reducing the susceptibility or severity of the virus, and 5) They believe that the anticipated benefits of doing preventive behaviours/actions outweigh the barriers. Perceived susceptibility was not a significant predictor of behavior in this study. This finding is consistent with studies that measured adherence to COVID-19 precautionary measures in China 22 and Korea. 23 , 24 A study that used a HBM framework to look at how Saudi Arabian students at Jazan University felt about the COVID-19 vaccine found that perceived susceptibility was not a good predictor of how they felt about getting the vaccine. 25 However, a similar study conducted in Malaysia found that high perceived susceptibility to COVID-19 infection was also associated with the behavior of vaccination intention. 26 A study that measured student behavior in the US related to non-pharmaceutical interventions (hand washing with soap and water, use of hand sanitizer, wearing a face mask in public, and practicing social distancing) found that perceived susceptibility was associated with multiple interventions more frequently. 27 A previous study conducted by Du Min e t al. found that low perceived risk was associated with vaccine hesitancy. 28 As with perceived susceptibility, perceived severity was not a significant predictor of preventive behavior in this study. Several studies regarding behavior change using BHM had similar results. 24 , 26 , 27 Perceived severity, on the other hand, was a significant predictor of preventive behaviors. 8 , 22 – 24 , 29 Perceived benefit is one predictor that is not a significant predictor of preventive behavior. This finding is inconsistent with several studies that used BHM to predict behavior change, particularly in relation to COVID-19 prevention 8 , 22 , 23 , 25 – 29 where it was found that there was no significant association between perceived benefit and preventive behaviour of. This study demonstrated that the perceived barrier significantly predicted COVID-19 preventive behaviour. This result is contrasts with behaviour study conducted in Sri Lanka and Iran, which established a significant positive relationship between perceived benefit and self-efficacy in COVID-19 prevention behaviour. 10 , 30 Nevertheless, these findings were congruent with a study conducted in Ethiopia that employed the HBM theory to assess student eating behaviour the US 31 and other behavioural studies in Iran, India, and Hong Kong. 8 , 17 , 32 This study also found that perceived self-efficacy was a significant predictor of COVID-19 preventive behaviour. The results show that, with lower self-efficacy, people were likely to practice COVID-19 prevention behaviour. This result is similar to several studies that examined behaviours using the BHM theory. Those studies found that perceived self-efficacy has a significant relationship to behaviour. 10 , 17 , 20 , 23 , 30 , 33 In contrast, a study that assessed the student’s behaviour on the non-pharmaceutical intervention of COVID-19 found that perceived self-efficacy was not a significant predictor of behaviour change. In theory, an individual with good self-efficacy tends to practice recommended action/behaviour, 13 which is the preventive behaviour of COVID-19. However, this study was unable to confirm these findings. The findings in this study illustrated that most respondents (97%) had no barriers to practising the recommended behaviour. Still most respondents (81%) were not confident that they could fully implement the recommended prevention behaviours. As many as 60% of respondents practised COVID-19 prevention behaviour well. In knowledge attitude practice (KAP) studies conducted in Indonesia, this finding (percentage of good behaviour) tended to be lower than the other two findings in Indonesia, where the rate of those who performed the correct behaviour for the prevention of COVID-19 was more than 90%. 34 , 35 Studies conducted in other countries also found that respondents who practised COVID-19 prevention behaviours were relatively high (>70%), such as in China, Nepal, Malaysia, Vietnam, and India. 36 – 40 Fundamentally, perceived susceptibility and severity affect how a person decides to act. 13 However, most respondents (84%) had low perceived susceptibility in this study. This means that most respondents did not believe that they were also at risk of being affected by COVID-19. This perception represented an obstacle for someone to implement recommended behaviour. It was also known that only 43% believed that if they were infected with COVID-19, they would experience harmful consequences for their bodies. Only half of the respondents believed that the recommended behaviour was able to protect them from COVID-19. This might relate to the information they obtained day-to-day. It was possible that most of them did not have a clear idea about the pathophysiology and epidemiology of COVID-19, made worse by unreliable news or hoaxes circulated on social media, which may have increased negative perceptions about COVID-19. 8 , 41 Regarding obstacles to implementing the recommended behaviour, a few respondents said it was extremely difficult to implement the behaviours (3%). This overall perception then leads to a low belief that respondents are able to implement the recommended behaviour. As a consequence, it will affect COVID-19 prevention behaviour practice. Perception is theoretically influenced by many factors, including demographics and level of knowledge. 8 , 10 , 13 , 20 , 34 , 35 A study conducted in China found that knowledge was influenced by educational level and domicile. 36 Good knowledge can form a good attitude, which then creates a good perceived. 42 , 43 COVID-19 was a pandemic that touched almost every facet of human existence. People had to adjust their daily routines to accommodate local government rules in order to reduce the virus transmission, and these behavioural shifts may last long after the disease has passed. 44 Increasing the respondents’ knowledge is essential to narrowing the gap between knowledge and practice, including myths, hoaxes circulating about COVID-19, and facts. Several important factors that may affect perceived self-efficacy are related to social norms and trust 45 in the community. Unfortunately, neither of these factors (including knowledge) was investigated in this study. The structural model of COVID-19 prevention behaviour in this study was not intended to describe the empirical phenomenon of preventive behaviour. Byrne stated that, if possible, researchers are advised to modify the model by using modification indices (MI) in SEM testing. He said that models with an MI score of more than 10 deserve attention for modification. 46 Already it has proved that to confront the increase in demand for care, the need for long-term care workforce, and the costs associated with care. 47 However, Hair et al. suggested that modifying the model should not change the model’s structure significantly. 19 These several studies found a gap in knowledge to inform changes in policies on infection-prevention measures in the community, community infection procedures, the frequency of testing, etc. 8 , 41 , 48 The unreliable information may increase the negative perception of COVID-19, leading to the community’s obedience to suggested preventive behaviors. 41 Moreover, people are more worried about their families and economic conditions due to the spreading pandemic than about complying with lockdown or restriction policies. Several factors that might be related were not examined in this study, such as level of knowledge, social norms, or trust. Study limitations Due to the social restrictions (the outdoor community activities are limited by the government based on the law announced every two weeks except for emergency purposes) in Indonesia, the data collection was conducted using a Google form without using a selected sampling method. Thus, the total number of respondents in this study was not representative of the total population in Indonesia and the entire territory of Indonesia. In addition, the entire population of Indonesia did not have the same opportunity to be selected as respondents due to limitations related to internet coverage and utilities. Other than that, since this study used google form to collect the data, researcher really relied on the respondent’s honesty and integrity. In order to limit the study respondents, the researchers already put inclusion criteria in the initial explanation section of the questionnaire that only those who are living in Indonesia and over 18 years old are eligible for the study. Therefore, we did not do any special treatment to control double entry and the location of respondents whether they live in Indonesia or outside the country. However, when we found it located outside of the country, we dropped the data from the data set. Because of the conditions described above, this study was vulnerable to information bias and selection bias. Regarding to the data collection process, we did not restrict respondent to just entry the form one time (in the system) so that there was possibility that one person will entry multiple times using different identity. Similar treatment conducted to clean the data, when researcher found double mobile number, we will randomly select one data and deleted the rest. We recommend to limit participants with “one email for one entry” to minimize the bias. As addition, this study can only describe the COVID-19 in just one time shot while the rate and impact of COVID-19 were changing rapidly over time. We recommend to employ longitudinal study for further since it can observe individual behavior change pattern/trend. Conclusions and recommendation This study found that more than half of the respondents still had low perceived susceptibility (84%) and severity (67%), but more than half had high perceived benefits (54%). Only a few respondents had significant barriers to implementing COVID-19 transmission prevention behaviours (3%). Still, most respondents had low perceived self-efficacy (81%), and only 60% had good behaviors related to COVID-19 prevention. It was found that sex had a statistically significant relationship with perceive susceptibility ( p -value = 0.000); perceived susceptibility had a statistically significant relationship with perceived severity ( p -value = 0.000); age ( p -value = 0.004), sex ( p -value = 0.030), and education (( p -value = 0.050) had significant relationships with perceived benefit; perceived benefit ( p -value = 0.000), resident ( p -value = 0.009), and sex ( p -value = 0.000) had a significant relationship with perceive barrier; and perceive barrier ( p -value = 0.000) and occupancy ( p -value = 0.040) had a significant relationship with perceive self-efficacy. Regarding behaviour, only perceived barriers ( p -value = 0.000) and self-efficacy ( p -value = 0.000) had statistically significant relationships with COVID-19 prevention behaviour. However, the structural model in this study was not fit to the data and was not fit to describe the empirical phenomenon under study. This study can be an input for public health policy development especially those related to behaviour change interventions/programs. By using HBM theory, policy makers or other stakeholders can consider which stages of behaviour change still require more intervention in addition to demographic factors which can also influence it. Based on HBM theory, for behaviour change to occur, individuals must perceive a threat in their current behaviour (perceived susceptibility and severity), believe that the change will bring meaningful or useful outcomes (perceived benefit), and possess the self-confidence to enact the change (self-efficacy). In this case, this research can be a reminder in terms of making evidence-based policies. In the context of COVID-19 preventive behaviour change, we recommend to improve perceived susceptibility and severity (since this study found low of perceived and severity) by providing the correct information about COVID-19 in the local cultural context. It is expected that by improving perceived susceptibility and severity, there would be an increase in respondents’ knowledge, increasing perceived susceptibility and severity. The results and concept of this study can also be used/implemented for developing prevention policies against many types of diseases that require community behaviour changes which consider stages of behaviour change. For further study, it is highly recommended to make inclusion and exclusion criteria prior to the data collection, to create a more rigorous data collection template to reduce selection bias effect, to collect the data onsite and if it is possible, to conduct the longitudinal study. The concept of HBM is one of the recommended theories to study about the health behaviour, hence it is replicable even in the different context of disease and area. Ethics approval and consent to participate The Commission approved this study for Research Ethics and Public Health Service, Faculty of Public Health, University of Indonesia Number: Ket-436/UN2.F10. D11/PPM.00.02/2021. Consent for publication Informed consent was obtained from all subjects involved in the study. Author contributions All authors made substantial contributions to this research and approved the final manuscript. TE and TS contributed to every step of the study (research concept, design, data interpretation, writing, and review). SP contributed to the research review. Data availability Dyrad. Data for: Community perception and COVID-19 prevention, https://doi.org/10.5061/dryad.pnvx0k6rp . 49 This project contains the following underlying data: • DATA_FINAL_tio-edit2.csv (Data include all variables in the questionnaire. The data contain 1802 respondents and 89 variables. The variable names of questions 1–23 were given according to the keyword in each question, while the variable names for question number 24–35 were specified according to the question’s number and its answer option. It is recommended to read the variable code definition in sheet 2 and the area code in sheet 3.) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). Figshare. IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA. https://protect-us.mimecast.com/s/qsa7CkRwomfYPpGvjC2JN1U?domain=doi.org 15 This project contains the following supplementary material: • Kuesioner Penelitian.pdf (the google questionnaire used in this study), https://doi.org/10.6084/m9.figshare.23292686.v2 . 50 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgments The authors are grateful to Prof. Dr. Dewi Susanna, dra, MS., who facilitated and supported the research process, and Universitas Indonesia for financial support. We also thankful to all respondents who were willing to involve in this study by signing the informed consent prior to answer all the questions. References 1. Bhattacharya S, Basu P, Poddar S: Changing epidemiology of SARS-CoV in the context of COVID-19 pandemic. J. Prev. Med. Hyg. 2020; 61 (2): E130–E136. PubMed Abstract | Publisher Full Text | Free Full Text 2. World Health Organization: WHO Coronavirus (COVID-19) Dashboard.2022. Accessed 8 February 2022. Reference Source 3. Indonesia TF of C-19: Distribution Map of COVID-19 Indonesia.2022. Accessed 8 February 2022. Reference Source 4. World Health Organisation: Advice for the public: Coronavirus disease (COVID-19). World Health Organization; 2021. Accessed 8 February 2022. Reference Source 5. Minister of Home Affairs of the Republic of Indonesia: Regulation of (the) Minister of Home Affairs of Republic Indonesia. 2021; Vol. 4 : pp. 1–2. 6. Task Force of COVID-19 Indonesia. Memahami Perilaku dan Informasi Tepatuntuk Mencegah Penularan COVID-19. Covid19.Go. Id. 2020. Accessed February 8, 2022. Reference Source 7. Task Force of COVID-19 Indonesia: Monitoring of Health Protocol Compliance.2021. Accessed 8 February 2022. Reference Source 8. Nguyen NPT, Hoang TD, Tran VT, et al. : Preventive behaviour of Vietnamese people in response to the COVID-19 pandemic. PLoS One. 2020; 15 (9 September): 1–11. PubMed Abstract | Publisher Full Text | Free Full Text 9. Lim JW, Gonzalez P, Wang-Letzkus MF, et al. : Understanding the cultural health belief model influencing health behaviours and health-related quality of life between Latina and Asian-American breast cancer survivors. Support Care Cancer. 2009; 17 (9): 1137–1147. PubMed Abstract | Publisher Full Text 10. Clark C, Davila A, Regis M, et al. : Predictors of COVID-19 voluntary compliance behaviours: An international investigation. Glob. Transitions. 2020; 2 : 76–82. PubMed Abstract | Publisher Full Text | Free Full Text 11. Jewell NP: Predictive Mathematical Models of the COVID-19 Pandemic Underlying Principles and Value of Projections. Am. Med. Assoc. J. 2020; 323 (19): 1893–1894. Publisher Full Text 12. Jose R, Narendran M, Bindu A, et al. : Public perception and preparedness for the pandemic COVID 19: A Health Belief Model approach. Clin. Epidemiol. Glob. Heal. 2021; 9 (9): 41–46. PubMed Abstract | Publisher Full Text | Free Full Text 13. Purnama SG, Susanna D: Attitude to COVID-19 Prevention with Large-Scale Social Restrictions (PSBB) in Indonesia: Partial Least Squares Structural Equation Modeling. Front. Public Health. 2020; 8 (October): 1–10. PubMed Abstract | Publisher Full Text | Free Full Text 14. Glanz K, Rimer B, Viswanath K: Health Behavior and Health Education. COT, editor. Fourth Ed.John Wiley & Sons, Ltd.; 2008. 15. Susanna D, Widyamurti W, Eryando T: Implementasi Of Social Policy Distancing In Effort Prevention COVID-19 In Indonesia. figshare. Journal Contribution. 2023. Publisher Full Text 16. Byrne BM: Structural Equation Modeling with Amos. Basic Concepts, Applications, and Programming.Third Ed. Taylor & Francis, New York; 2016. 17. Hair JF, Anderson RE, Tatham RL, et al. : Multivariate Data Analysis. Eighth Ed.Annabel Ainscow; 2019. 18. Basu A: Structural equation modelling with lavaan: a tutorial and an intuitive introduction.2021 [cited 2023 Jun 20]. Reference Source 19. Mahindarathne PP: Assessing COVID-19 preventive behaviours using the health belief model: A Sri Lankan study. J. Taibah Univ. Med. Sci. 2021; 16 (6): 914–919. PubMed Abstract | Publisher Full Text | Free Full Text 20. Rea LM, Parker RA: Designing and Conducting Survey Research. A Comprehensive Guide. Jossey-Bass A Wiley Brand; 2014. 21. Commodari E, Lucia LR: Valentina: Adolescent in Quarantine During COVID-19 Pandemic in Italy: Perceived Health Risk, Beliefs, Psychological Experiences and Expectations for the Future. Front. Psychol. 2020; 11 : 559951. PubMed Abstract | Publisher Full Text | Free Full Text 22. Kline RB: Principles and Practice of Structural Equation Modelling. Fourth Ed.The Guilford Press; 2016. 23. Tadesse T, Alemu T, Amogne G, et al. : Predictors of coronavirus disease 2019 (Covid-19) prevention practices using health belief model among employees in Addis Ababa, Ethiopia, 2020. Infect. Drug Resist. 2020; 13 : 3751–3761. Publisher Full Text 24. Shahnazi H, Ahmadi-Livani M, Pahlavanzadeh B, et al. : Assessing preventive health behaviors from COVID-19: a cross-sectional study with health belief model in Golestan Province, Northern of Iran. Infect. Dis. Poverty. 2020; 9 (1): 157–159. PubMed Abstract | Publisher Full Text | Free Full Text 25. Tong KK, Chen JH, Yu EY, et al. : Adherence to COVID-19 Precautionary Measures: Applying the Health Belief Model and Generalised Social Beliefs to a Probability Community Sample. Appl. Psychol. Heal. Well-Being. 2020; 12 (4): 1205–1223. PubMed Abstract | Publisher Full Text | Free Full Text 26. Kim S, Kim S: Analysis of the impact of health beliefs and resource factors on preventive behaviors against the Covid-19 pandemic. Int. J. Environ. Res. Public Health. 2020; 17 (22): 1–21. PubMed Abstract | Publisher Full Text | Free Full Text 27. Park JH, Cheong HK, Son DY, et al. : Perceptions and behaviors related to hand hygiene for the prevention of H1N1 influenza transmission among Korean university students during the peak pandemic period. BMC Infect. Dis. 2010; 10 : 1–8. PubMed Abstract | Publisher Full Text | Free Full Text 28. Khalafalla HE, Tumambeng MZ, Halawi MHA, et al. : COVID-19 Vaccine Hesitancy Prevalence and Predictors among the Students of Jazan University, Saudi Arabia Using the Health Belief Model: A Cross-Sectional Study. Vaccines. 2022; 10 (2). PubMed Abstract | Publisher Full Text | Free Full Text 29. Wong LP, Alias H, Wong PF, et al. : The use of the health belief model to assess predictors of intent to receive the COVID-19 vaccine and willingness to pay. Hum. Vaccin. Immunother. 2020; 16 (9): 2204–2214. PubMed Abstract | Publisher Full Text | Free Full Text 30. Hatteberg SJ, Kollath-Cattano C: Applying the health belief model to examine college students’ early-stage adoption of non-pharmaceutical interventions in response to COVID-19. J. Am. Coll. Heal. 2022; 1–6. PubMed Abstract | Publisher Full Text 31. Du M, Tao L, Liu J: Association between risk perception and influenza vaccine hesitancy for children among reproductive women in China during the COVID-19 pandemic: a national online survey. BMC Public Health. 2022; 22 (1): 310–385. PubMed Abstract | Publisher Full Text | Free Full Text 32. Ejeta LT, Ardalan A, Paton D, et al. : Predictors of community preparedness for flood in Dire-Dawa town, Eastern Ethiopia: Applying an adapted version of Health Belief Model. Int. J. Disaster Risk Reduct. 2016; 19 (September): 341–354. Publisher Full Text 33. Karimy M, Azarpira H, Araban M: Using health belief model constructs to examine differences in adherence to pap test recommendations among Iranian women. Asian Pac. J. Cancer Prev. 2017; 18 (5): 1389–1394. PubMed Abstract | Publisher Full Text | Free Full Text 34. Kim HS, Ahn J, No JK: Applying the health belief model to college students’ health behaviour. Nutr. Res. Pract. 2012; 6 (6): 551–558. Publisher Full Text 35. Wong MCS, Wong ELY, Huang J, et al. : Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong.2020; 12.083 (January): 1148–1156. 36. Ansari-Moghaddam A, Seraji M, Sharafi Z, et al. : The protection motivation theory for predicting intention of COVID-19 vaccination in Iran: a structural equation modeling approach. BMC Public Health. 2021; 21 (1): 1–9. PubMed Abstract | Publisher Full Text | Free Full Text 37. Muslih M, Susanti HD, Rias YA, et al. : Knowledge, attitude, and practice of Indonesian residents toward Covid-19: A cross-sectional survey. Int. J. Environ. Res. Public Health. 2021; 18 (9). Publisher Full Text 38. Yanti B, Wahyudi E, Wahiduddin W, et al. : Community Knowledge, Attitudes, and Behavior Towards Social Distancing Policy as Prevention Transmission of Covid-19 in Indonesia. J. AdmKesehatIndones. 2020; 8 (2): 4. Publisher Full Text 39. Gao H, Hu R, Yin L, et al. : Knowledge, attitudes and practices of the Chinese public with respect to coronavirus disease (COVID-19): An online cross-sectional survey. BMC Public Health. 2020; 20 : 1–8. PubMed Abstract | Publisher Full Text | Free Full Text 40. Hussain A, Garima T, Singh BM, et al. : Knowledge, attitudes, and practices towards COVID-19 among Nepalese Residents: A quick online cross-sectional survey. Asian J. Med. Sci. 2020; 11 (3): 6–11. Publisher Full Text 41. Zhong BL, Luo W, Li HM, et al. : Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. Int. J. Biol. Sci. 2020; 16 (10): 1745–1752. PubMed Abstract | Publisher Full Text | Free Full Text 42. Azlan AA, Hamzah MR, Sern TJ, et al. : Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS One. 2020; 15 (5): 1–15. PubMed Abstract | Publisher Full Text | Free Full Text 43. Narayana G, Pradeepkumar B, Dasaratha J: Knowledge, perception, and practices towards COVID-19 pandemic among the general public of India: A cross-sectional online survey. Curr. Med. Res. Pract. 2020; 10 (2020): 153–159. PubMed Abstract | Publisher Full Text | Free Full Text 44. Daragmeh A, Sági J, Zéman Z: Continuous intention to use e-wallet in the context of the covid-19 pandemic: Integrating the health belief model (hbm) and technology continuous theory (tct). J. Open Innov.: Technol. Mark. Complex. 2021 May 12; 7 (2): 132. Publisher Full Text 45. Hussain MW, Mirza T, Hassan MM: Impact of COVID-19 Pandemic on the Human Behavior. Int. J. Educ. Manag. Eng. 2020; 10 (6): 35–61. Publisher Full Text 46. Ajilore K, Atakiti I, Onyenankeya K: College students’ knowledge, attitudes and adherence to public service announcements on Ebola in Nigeria: Suggestions for improving future Ebola prevention education programmes. Health Educ. J. 2017; 76 (6): 648–660. Publisher Full Text 47. Martinez-Lacoba R, Pardo-Garcia I, Escribano-Sotos F: Aging, dependence, and long-term care: A systematic review of employment creation. Inquiry. 2021 Dec 16; 58 : 00469580211062426. PubMed Abstract | Publisher Full Text | Free Full Text 48. Tachfouti N, Slama K, Berraho M, et al. : The impact of knowledge and attitudes on adherence to tuberculosis treatment: A case-control study in a Moroccan region. Pan Afr. Med. J. 2012; 12 (1): 1–8. Publisher Full Text 49. Susanna D, Widyamurti W, Eryando T: Data for: Community perception and COVID-19 prevention. Dataset. Dryad. 2023. Publisher Full Text 50. Susanna D, Widyamurti W, Eryando T: IMPLEMENTATION OF SOCIAL POLICY DISTANCING IN EFFORT PREVENTION COVID-19 IN INDONESIA. Journal Contribution. figshare. 2023. Publisher Full Text Comments on this article Comments (1) Version 3 VERSION 3 PUBLISHED 03 Oct 2024 Revised Comment ADD YOUR COMMENT Version 1 VERSION 1 PUBLISHED 10 Aug 2023 Discussion is closed on this version, please comment on the latest version above. Author Response 18 Jan 2024 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 18 Jan 2024 Author Response Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Competing Interests: No competing interests were disclosed. Close Report a concern Discussion is closed on this version, please comment on the latest version above. Author details Author details 1 Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, West Java, 16424, Indonesia 2 Research and Innovations, Lincoln University College,, Petaling Jaya, Selangor, 47301, Malaysia Tris Eryando Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Visualization, Writing – Review & Editing Tiopan Sipahutar Roles: Data Curation, Formal Analysis, Methodology, Visualization, Writing – Original Draft Preparation Sandeep Poddar Roles: Visualization, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research was funded by Universitas Indonesia under contract number NKB-630/UN2.RST/HKP.05.00/2022 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 03 Oct 2024, 12:966 https://doi.org/10.12688/f1000research.135262.3 version 2 Revised Published: 28 Dec 2023, 12:966 https://doi.org/10.12688/f1000research.135262.2 version 1 Published: 10 Aug 2023, 12:966 https://doi.org/10.12688/f1000research.135262.1 Copyright © 2024 Eryando T et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Eryando T, Sipahutar T and Poddar S. Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.12688/f1000research.135262.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 03 Oct 2024 Revised Views 0 Cite How to cite this report: Husnah H. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.172626.r328969 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v3#referee-response-328969 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 21 Oct 2024 Husnah Husnah , Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.172626.r328969 Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] Based on the latest revision that I have studied, there ... Continue reading READ ALL Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] Based on the latest revision that I have studied, there are several suggestions for improvement. A. Background The background and methods are appropriate B. Results 1. The results of the research need to be supplemented with data about the number of respondents written down, most of which should be called numbers, not just percentages. Example: More than halfof respondents should write 901 (50%) instead of 1802 2. The value of a significant relationship should be written down, not just p <0.05 C. Conclusion In the sentence respondents have a low perception of vulnerability (84%) and severity (67%), it is better to add the number of respondents, then write the percentage Competing Interests: No competing interests were disclosed. Reviewer Expertise: Nutrition Department I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Husnah H. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.172626.r328969 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v3#referee-response-328969 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 28 Dec 2023 Revised Views 0 Cite How to cite this report: Oguntayo R. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r233985 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-233985 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 05 Sep 2024 Rotimi Oguntayo , Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico Approved VIEWS 0 https://doi.org/10.5256/f1000research.159876.r233985 Dear Authors! Thank you for attending to all my questions, and the amendment ... Continue reading READ ALL Dear Authors! Thank you for attending to all my questions, and the amendment made to the article is commendable. At this point, I recommend that this work be indexed. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Psychology (Crises Prevention and management) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Oguntayo R. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r233985 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-233985 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: La Rosa VL. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r296323 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-296323 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Jul 2024 Valentina Lucia La Rosa , Department of Educational Sciences, University of Catania, Catania, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.159876.r296323 The manuscript “Community Preventive Behaviour and Perception on the Severity of COVID-19 Disease in Indonesia, 2021-2022: Structural Equation Modelling" provides a valuable contribution to the understanding of COVID-19 perceptions and preventive behaviors in Indonesia. The use of HBM and SEM ... Continue reading READ ALL The manuscript “Community Preventive Behaviour and Perception on the Severity of COVID-19 Disease in Indonesia, 2021-2022: Structural Equation Modelling" provides a valuable contribution to the understanding of COVID-19 perceptions and preventive behaviors in Indonesia. The use of HBM and SEM provides a robust framework for their analysis. However, addressing the revisions outlined above will significantly strengthen the manuscript and improve its suitability for indexed. The Authors should provide a broader theoretical basis for their SEM analysis. The HBM is a well-established model, but the authors need to explicitly link their research questions and hypotheses to the specific constructs of the HBM. In addition, they should discuss how the HBM influenced their choice of variables and the relationships tested in their SEM model. The Authors need to provide more details about their data collection methods. In particular, they should address how they ensured that respondents did not complete the Google form multiple times and how they ensured an even distribution of respondents across the 38 provinces of Indonesia. If these measures were not taken, this point should be acknowledged as a limitation of the study. The goodness-of-fit indices suggest that the proposed model does not adequately fit the data. The authors should consider revising the model by exploring alternative constructs or pathways that may better represent the empirical data. The Authors are required to provide a detailed explanation of the steps taken to address issues with the model fit and any changes that were made to improve the model. The Authors should expand the discussion of the implications of their findings, with particular focus on the non significant predictors, and discuss how these findings contribute to our understanding of preventive behaviors in the context of COVID-19. I suggest including references to studies that have examined the perceived health risk associated with COVID-19 in different populations, such as adolescents and college students, to provide a broader context and comparison for your findings. Some of these studies include: 10.3389/fpsyg.2020.559951; 10.3390/ejihpe13080108. The manuscript should be thoroughly proofread to correct grammatical errors and improve its readability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Commodari E, La Rosa VL: Adolescents in Quarantine During COVID-19 Pandemic in Italy: Perceived Health Risk, Beliefs, Psychological Experiences and Expectations for the Future. Front Psychol . 2020; 11 : 559951 PubMed Abstract | Publisher Full Text 2. La Rosa VL, Commodari E: University Experience during the First Two Waves of COVID-19: Students' Experiences and Psychological Wellbeing. Eur J Investig Health Psychol Educ . 2023; 13 (8): 1477-1490 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Development in the life span and the impact of critical events such as the COVID-19 pandemic I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT La Rosa VL. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r296323 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-296323 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 03 Oct 2024 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 03 Oct 2024 Author Response Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. ... Continue reading Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article. Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article. Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly. Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper. We added reference as suggested. Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article. Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article. Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly. Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper. We added reference as suggested. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 03 Oct 2024 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 03 Oct 2024 Author Response Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. ... Continue reading Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article. Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article. Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly. Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper. We added reference as suggested. Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article. Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article. Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly. Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper. We added reference as suggested. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 10 Aug 2023 Views 0 Cite How to cite this report: Oguntayo R. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r201070 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-201070 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 22 Sep 2023 Rotimi Oguntayo , Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.148373.r201070 Abstract (Minor Revision): In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate ... Continue reading READ ALL Abstract (Minor Revision): In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self-efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related? Methodology (Major Revision): This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception of the severity of COVID-19 disease in Indonesia. As with other research articles published in various journals, your research article needs significant improvement to enhance its readability and credibility. You collected data through a Google Form distributed via various social media channels, resulting in thousands of responses. However, it is essential to address potential biases in data collection. How did you ensure that respondents didn't fill out the Google Form multiple times? Was there strict control over the form submissions? Additionally, you mentioned that respondents were spread across Indonesia. How did you ensure even distribution among Indonesia's 38 provinces? Please provide details or references for this distribution method. If you were unable to meet up with any of these criteria, list them as your study limitations. Study and Sample (Minor Revision): This research aims to reach the Indonesian public, including contributions from social media influencers. To ensure the research's representativeness, please clarify how you ensured that the sample accurately represents all Indonesians. Your respondents are identified as Indonesian citizens, but how did you verify their citizenship? What measures were in place to prevent participation by non-Indonesian residents? Consider addressing these potential sources of bias. If you were unable to meet up with any of these criteria, list them as your study limitations. Conceptual Model (Major Revision): This research employs the Health Belief Model (HBM) as the primary theoretical framework. It is crucial to emphasize that Structural Equation Modeling (SEM) relies on a well-established theory to validate models using real data. The direction of relationships in the model must align with theoretical support. Additionally, theoretical underpinnings are essential in determining the appropriate sample size for SEM and ensuring sample adequacy when testing a theory-based model. Please provide a more comprehensive, precise and concise theoretical foundation for your SEM analysis. Grammatical Errors (Minor Revision): Correct the English writing errors in the article generally. Moreover, consider updating the COVID-19 incidence rate beyond the end of 2021, extending it into 2022, to enhance the study's accuracy and encompassing quality. Results: The results section requires elaboration and clarification to explain how you arrived at the conclusion regarding the relationship between self-efficacy and perceived influence on preventive behavior for readers who not statistic expert to be able to read and understand while retaining the journal format. Discussion: Expand on the implications of the results and how they relate to the theoretical framework. Discuss the broader significance of your findings in the context of COVID-19 prevention in Indonesia and HBM concept for clinical practice in local context. Also, consider inclusion of more recent similar COVID-19 incidence data in other country to buttress your results in this section. Conclusion with Limitations and Implications: Summarize the key findings, discuss the study’s limitations as suggested above, and provide implications for clinical practice or public health initiatives, and future research to ascertain replicability of your work. Indexing of the Paper: The indexing of the paper is contingent upon addressing the major revisions outlined in the methodology and conceptual model sections. Minor revisions should also be completed in the abstract, study and sample, grammatical errors, and the inclusion of more recent COVID-19 incidence data in the discussion section. Once these revisions are made, the paper will be reevaluated for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Psychology (Crises Prevention and management) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Oguntayo R. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r201070 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-201070 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 Dec 2023 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 28 Dec 2023 Author Response Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 Dec 2023 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 28 Dec 2023 Author Response Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Husnah H. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r195846 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-195846 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Sep 2023 Husnah Husnah , Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.148373.r195846 1. Abstract (minor revision) In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in ... Continue reading READ ALL 1. Abstract (minor revision) In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related? 2. Methods (major revision) This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception on the severity of COVID-19 disease in Indonesia . As with other research articles that have been published in various journals, of course your research article needs to be improved to make it worth reading. Your research obtained data from a google form that was distributed through various social media. You managed to get thousands of responses. But I am curious how can you ensure that one respondent does not fill in the google form more than once? Is there strict control over the filling of google forms by respondents? I hope that your data does not become biased because of this. Then you mentioned that the respondents were spread across Indonesia. How do you ensure that the respondents are indeed spread evenly across Indonesia, which consists of 38 provinces? Perhaps you have your own reference in determining the distribution throughout Indonesia as you mentioned, please explain. 3. Study and Sample (minor revision) This research expands to the Indonesian public with contributions from social media influencers. How do you ensure this research is representative of all Indonesians? Respondents participating in your research are Indonesian citizens. How do you ensure that the people filling in the data are Indonesian citizens? What if a foreigner residing in Indonesia receives and fills out a questionnaire from your research, how do you know? 4. Conceptual Model (major revision) This research uses HBM as the main reference. What should be a common concern in using SEM must be based on a certain theory. This is because the function of SEM is to confirm the model formed on real data obtained in the field. In addition, the determination of the direction of the relationship built in the model must be theoretically corroborated. This will also affect the determination of the amount of data or research samples that are suitable for use in SEM and feasible in terms of sample adequacy in testing a theory-based model. 5. Minor revision ​Correct the English writing errors in the article. I have marked them on the linked annotated article . For example, in the questionnaire table in the methods section , and in the discussion section. The COVID-19 incidence rate has been mentioned from mid-2021 to the end of 2021. To add to the accuracy of this study, the incidence rate of COVID-19 until 2022 should also be attached. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Nutrition Department I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Husnah H. Reviewer Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r195846 ) The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-195846 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (1) Version 3 VERSION 3 PUBLISHED 03 Oct 2024 Revised Comment ADD YOUR COMMENT Version 1 VERSION 1 PUBLISHED 10 Aug 2023 Discussion is closed on this version, please comment on the latest version above. Author Response 18 Jan 2024 Tris Eryando , Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia 18 Jan 2024 Author Response Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be ... Continue reading Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document Competing Interests: No competing interests were disclosed. Close Report a concern Discussion is closed on this version, please comment on the latest version above. keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 03 Oct 24 read Version 2 (revision) 28 Dec 23 read read Version 1 10 Aug 23 read read Husnah Husnah , Syiah Kuala University, Banda Aceh, Indonesia Rotimi Oguntayo , Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Mexico Valentina Lucia La Rosa , University of Catania, Catania, Italy Comments on this article All Comments (1) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Husnah H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 21 Oct 2024 | for Version 3 Husnah Husnah , Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia 0 Views copyright © 2024 Husnah H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] Based on the latest revision that I have studied, there are several suggestions for improvement. A. Background The background and methods are appropriate B. Results 1. The results of the research need to be supplemented with data about the number of respondents written down, most of which should be called numbers, not just percentages. Example: More than halfof respondents should write 901 (50%) instead of 1802 2. The value of a significant relationship should be written down, not just p <0.05 C. Conclusion In the sentence respondents have a low perception of vulnerability (84%) and severity (67%), it is better to add the number of respondents, then write the percentage Competing Interests No competing interests were disclosed. Reviewer Expertise Nutrition Department I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Husnah H. Peer Review Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.172626.r328969) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-966/v3#referee-response-328969 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Oguntayo R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 05 Sep 2024 | for Version 2 Rotimi Oguntayo , Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico 0 Views copyright © 2024 Oguntayo R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Authors! Thank you for attending to all my questions, and the amendment made to the article is commendable. At this point, I recommend that this work be indexed. Competing Interests No competing interests were disclosed. Reviewer Expertise Psychology (Crises Prevention and management) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Oguntayo R. Peer Review Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r233985) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-233985 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 La Rosa V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Jul 2024 | for Version 2 Valentina Lucia La Rosa , Department of Educational Sciences, University of Catania, Catania, Italy 0 Views copyright © 2024 La Rosa V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript “Community Preventive Behaviour and Perception on the Severity of COVID-19 Disease in Indonesia, 2021-2022: Structural Equation Modelling" provides a valuable contribution to the understanding of COVID-19 perceptions and preventive behaviors in Indonesia. The use of HBM and SEM provides a robust framework for their analysis. However, addressing the revisions outlined above will significantly strengthen the manuscript and improve its suitability for indexed. The Authors should provide a broader theoretical basis for their SEM analysis. The HBM is a well-established model, but the authors need to explicitly link their research questions and hypotheses to the specific constructs of the HBM. In addition, they should discuss how the HBM influenced their choice of variables and the relationships tested in their SEM model. The Authors need to provide more details about their data collection methods. In particular, they should address how they ensured that respondents did not complete the Google form multiple times and how they ensured an even distribution of respondents across the 38 provinces of Indonesia. If these measures were not taken, this point should be acknowledged as a limitation of the study. The goodness-of-fit indices suggest that the proposed model does not adequately fit the data. The authors should consider revising the model by exploring alternative constructs or pathways that may better represent the empirical data. The Authors are required to provide a detailed explanation of the steps taken to address issues with the model fit and any changes that were made to improve the model. The Authors should expand the discussion of the implications of their findings, with particular focus on the non significant predictors, and discuss how these findings contribute to our understanding of preventive behaviors in the context of COVID-19. I suggest including references to studies that have examined the perceived health risk associated with COVID-19 in different populations, such as adolescents and college students, to provide a broader context and comparison for your findings. Some of these studies include: 10.3389/fpsyg.2020.559951; 10.3390/ejihpe13080108. The manuscript should be thoroughly proofread to correct grammatical errors and improve its readability. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Commodari E, La Rosa VL: Adolescents in Quarantine During COVID-19 Pandemic in Italy: Perceived Health Risk, Beliefs, Psychological Experiences and Expectations for the Future. Front Psychol . 2020; 11 : 559951 PubMed Abstract | Publisher Full Text 2. La Rosa VL, Commodari E: University Experience during the First Two Waves of COVID-19: Students' Experiences and Psychological Wellbeing. Eur J Investig Health Psychol Educ . 2023; 13 (8): 1477-1490 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Development in the life span and the impact of critical events such as the COVID-19 pandemic I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 03 Oct 2024 Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia Response to no. 1: We propose a comprehensive approach to understanding behavior change using the Health Belief Model (HBM), as opposed to focusing to understand behaviour change partially. The HBM offers a holistic perspective on behaviour change processes. However, when utilizing HBM as a theoretical framework, there were latent variables that did not directly observed. These latent variables are represented by various indicators/observe variable. Consequently, Structural Equation Modelling (SEM) is the most suitable analytical method for examining relationships between variables (latent variable and measurement/indicator) within the HBM framework, as it allows for the analysis of both latent variables and the overall model as a single, cohesive unit. We added the additional statement in the article. Response to no. 2: We did not restrict respondents to just entry the form one time. However, once the data collected and stored in to the database, researcher did data checking particularly to identify double/triple number. The researcher selected one data randomly and deleted the rest with the same number. There was possibility that one person will entry multiple times using different identity. We added the additional statement in the article. Response to no. 3: Unfortunately, we didn’t develop or explore the alternative construct after finding that the model did not fit the data since the aim of this study was to examine changes in community behaviour to prevent local COVID-19 transmissions and changes in community perceptions about the severity level of COVID-19. This study also intended to prove whether the construct proposed can predict the behaviour or not and the result showed that it could not. We did not re-construct the model considering Hair et al. suggestion that modifying the model should not change the model’s structure significantly. Response to no. 4: In order to propose the good model, we have explained the one by one in the paper which include identifying degree of freedom, construct validity, discriminant validity, construct reliability, dependency and goodness of fit index. During the process, we excluded 2 variable that disturbed the data. However, when the model builds and tested with the goodness of fit index, we did not continue to re-structure the model. We refer to Hair et al. suggestion that modifying the model should not change the model’s structure significantly. We already stated it in the paper. We added reference as suggested. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern La Rosa VL. Peer Review Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.159876.r296323) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-966/v2#referee-response-296323 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Oguntayo R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 22 Sep 2023 | for Version 1 Rotimi Oguntayo , Department of Psychology, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, Chihuahua, Mexico 0 Views copyright © 2023 Oguntayo R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Abstract (Minor Revision): In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self-efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related? Methodology (Major Revision): This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception of the severity of COVID-19 disease in Indonesia. As with other research articles published in various journals, your research article needs significant improvement to enhance its readability and credibility. You collected data through a Google Form distributed via various social media channels, resulting in thousands of responses. However, it is essential to address potential biases in data collection. How did you ensure that respondents didn't fill out the Google Form multiple times? Was there strict control over the form submissions? Additionally, you mentioned that respondents were spread across Indonesia. How did you ensure even distribution among Indonesia's 38 provinces? Please provide details or references for this distribution method. If you were unable to meet up with any of these criteria, list them as your study limitations. Study and Sample (Minor Revision): This research aims to reach the Indonesian public, including contributions from social media influencers. To ensure the research's representativeness, please clarify how you ensured that the sample accurately represents all Indonesians. Your respondents are identified as Indonesian citizens, but how did you verify their citizenship? What measures were in place to prevent participation by non-Indonesian residents? Consider addressing these potential sources of bias. If you were unable to meet up with any of these criteria, list them as your study limitations. Conceptual Model (Major Revision): This research employs the Health Belief Model (HBM) as the primary theoretical framework. It is crucial to emphasize that Structural Equation Modeling (SEM) relies on a well-established theory to validate models using real data. The direction of relationships in the model must align with theoretical support. Additionally, theoretical underpinnings are essential in determining the appropriate sample size for SEM and ensuring sample adequacy when testing a theory-based model. Please provide a more comprehensive, precise and concise theoretical foundation for your SEM analysis. Grammatical Errors (Minor Revision): Correct the English writing errors in the article generally. Moreover, consider updating the COVID-19 incidence rate beyond the end of 2021, extending it into 2022, to enhance the study's accuracy and encompassing quality. Results: The results section requires elaboration and clarification to explain how you arrived at the conclusion regarding the relationship between self-efficacy and perceived influence on preventive behavior for readers who not statistic expert to be able to read and understand while retaining the journal format. Discussion: Expand on the implications of the results and how they relate to the theoretical framework. Discuss the broader significance of your findings in the context of COVID-19 prevention in Indonesia and HBM concept for clinical practice in local context. Also, consider inclusion of more recent similar COVID-19 incidence data in other country to buttress your results in this section. Conclusion with Limitations and Implications: Summarize the key findings, discuss the study’s limitations as suggested above, and provide implications for clinical practice or public health initiatives, and future research to ascertain replicability of your work. Indexing of the Paper: The indexing of the paper is contingent upon addressing the major revisions outlined in the methodology and conceptual model sections. Minor revisions should also be completed in the abstract, study and sample, grammatical errors, and the inclusion of more recent COVID-19 incidence data in the discussion section. Once these revisions are made, the paper will be reevaluated for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Psychology (Crises Prevention and management) I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 28 Dec 2023 Tris Eryando, Department of Biostatistics and Population, Faculty of Public Health, Universitas Indonesia, Depok, 16424, Indonesia Thank you for your valuable inputs to this paper. We already revised almost all part according to your input. Following are our response: Abstract The abstract session will be revised according to the article’s revision. The number will be put accordingly. The conclusion of relationship between self- efficacy and perceived was made based on statistical test. It will be added in the abstract. Methodology Due to the social restriction during the pandemic, the data was collected using google form. We did not do any specific treatment to limit the frequency of people submission as well as the respondents location. We also could not prevent respondent from other country to fill the link. However, when we did data cleaning, we erased data which is not located in Indonesia and having the same identity. Because of data in this study was collected through google link, the respondent indeed did not distributed evenly in all area in Indonesia and not to mention to represent and that is for sure could not represent all Indonesian population. Thus, this study is vulnerable to information and selection bias. All this information will be added in the article as our study limitation including our suggestion to minimize the bias for the further study. Study and sample As we already wrote down in limitation part, sample in this study is not representing Indonesian population. We only distributed the link as much as we can and even engage with social media influencer to speed up the distribution. We wanted to collect as much as we can during the data collection time with the minimum sample size as our benchmark. As mentioned previously, we cannot control response from other country, as well as double respondent, however when we did data cleaning, we dropped out respondent who are not located in Indonesia and having the same identity. Indeed, this situation somehow my lead to the information and selection bias. Hence, we also provided our recommendation for further study. Conceptual model This study employed HBM theory as the conceptual framework and the data was collected according to each variable mentioned in the theory. The framework was described in the article both in Figure 1 and Table 1. The point of using SEM is to test a theory by specifying a model that represent predictions of that theory among plausible constructs measured with appropriate observed variables. In this study, we tested the HBM theory using the collected data. The output of this analysis is to identify whether the analysis deals with substantive theoretical issues regardless of whether or not a model is retained. Thus, in principle, the analysis in this study did not highlight/emphasize the statistical relationship among 2 variables, instead a model as a whole. We will add the explanation in the paper, Grammatical Error, Result, Discussion, and Conclusion with Limitations and Implications >> we directly revised in the document View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Oguntayo R. Peer Review Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r201070) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-201070 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2023 Husnah H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Sep 2023 | for Version 1 Husnah Husnah , Department of Nutrition, School of Medicine, Syiah Kuala University, Banda Aceh, Aceh, Indonesia 0 Views copyright © 2023 Husnah H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Abstract (minor revision) In the conclusion section, significant results have been attached to preventive behavior and community perceptions of the severity of COVID-19 disease, but to support objective data, it is necessary to attach data in the form of accurate numbers in accordance with the findings in the field. In the results section, you mentioned that there is a relationship between self efficacy and perceived influence on preventive behavior. How did you come to the conclusion that these variables are related? 2. Methods (major revision) This is an interesting study because it is a large-scale study of the country of Indonesia. I have high hopes that this research will be successfully indexed so that it can be used as a reference by other studies in information about Community preventive behavior and perception on the severity of COVID-19 disease in Indonesia . As with other research articles that have been published in various journals, of course your research article needs to be improved to make it worth reading. Your research obtained data from a google form that was distributed through various social media. You managed to get thousands of responses. But I am curious how can you ensure that one respondent does not fill in the google form more than once? Is there strict control over the filling of google forms by respondents? I hope that your data does not become biased because of this. Then you mentioned that the respondents were spread across Indonesia. How do you ensure that the respondents are indeed spread evenly across Indonesia, which consists of 38 provinces? Perhaps you have your own reference in determining the distribution throughout Indonesia as you mentioned, please explain. 3. Study and Sample (minor revision) This research expands to the Indonesian public with contributions from social media influencers. How do you ensure this research is representative of all Indonesians? Respondents participating in your research are Indonesian citizens. How do you ensure that the people filling in the data are Indonesian citizens? What if a foreigner residing in Indonesia receives and fills out a questionnaire from your research, how do you know? 4. Conceptual Model (major revision) This research uses HBM as the main reference. What should be a common concern in using SEM must be based on a certain theory. This is because the function of SEM is to confirm the model formed on real data obtained in the field. In addition, the determination of the direction of the relationship built in the model must be theoretically corroborated. This will also affect the determination of the amount of data or research samples that are suitable for use in SEM and feasible in terms of sample adequacy in testing a theory-based model. 5. Minor revision ​Correct the English writing errors in the article. I have marked them on the linked annotated article . For example, in the questionnaire table in the methods section , and in the discussion section. The COVID-19 incidence rate has been mentioned from mid-2021 to the end of 2021. To add to the accuracy of this study, the incidence rate of COVID-19 until 2022 should also be attached. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Nutrition Department I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Husnah H. Peer Review Report For: Community preventive behaviour and perception on the severity of COVID-19 disease in Indonesia, 2021-2022: Structural equation modelling [version 3; peer review: 1 approved, 2 approved with reservations] . F1000Research 2024, 12 :966 ( https://doi.org/10.5256/f1000research.148373.r195846) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-966/v1#referee-response-195846 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Community preventive behaviour and perception...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/12-966/v3" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/12-966/v3&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/12-966/v3" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Eryando T et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/12-966/v3/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/12-966", templates : { twitter : "Community preventive behaviour and perception on the severity.... Eryando T et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/12-966/v3" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/135262/172626") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "172626"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "195842": 0, "296324": 0, "195843": 0, "233986": 0, "296325": 0, "233985": 9, "296326": 0, "195841": 0, "296327": 0, "195846": 26, "317184": 0, "300672": 0, "195847": 0, "317185": 0, "300673": 0, "195844": 0, "317186": 0, "300674": 0, "195845": 0, "300675": 0, "296323": 17, "296332": 0, "195848": 0, "328969": 4, "296328": 0, "296329": 0, "328971": 0, "296330": 0, "328970": 0, "296331": 0, "288788": 0, "288789": 0, "288790": 0, "288791": 0, "288785": 0, "288786": 0, "288787": 0, "288792": 0, "288793": 0, "288794": 0, "269493": 0, "269494": 0, "269495": 0, "269500": 0, "269244": 0, "269501": 0, "269496": 0, "269498": 0, "269499": 0, "269243": 0, "280780": 0, "280781": 0, "280782": 0, "280783": 0, "280776": 0, "280777": 0, "280778": 0, "280779": 0, "280784": 0, "280785": 0, "201063": 0, "201062": 0, "201061": 0, "201067": 0, "201066": 0, "201065": 0, "201064": 0, "201070": 32, "201069": 0, "201068": 0, "300668": 0, "300669": 0, "317182": 0, "300670": 0, "317183": 0, "300671": 0, "300666": 0, "300667": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "f2886d2b-f567-4ac5-85e0-f85ec51a2cca"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });