Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine learning and statistical analysis

Determinants of COVID-19 vaccine hesitancy in... | 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/13-1241" }, "headline": "Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine...", "datePublished": "2024-10-17T11:49:42", "dateModified": "2024-10-17T11:49:42", "author": [ { "@type": "Person", "name": "Ayesha Naseem" }, { "@type": "Person", "name": "Fatima Rasool" }, { "@type": "Person", "name": "Furqan K. Haashmi" }, { "@type": "Person", "name": "Muhammad Hamza Shoaib" }, { "@type": "Person", "name": "Wayne G. Carter" } ], "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 Despite the availability of a vaccine to combat COVID-19 disease, vaccine hesitancy is still a major concern, notably in countries with developing healthcare systems such as Pakistan. Hence, this study considered the rationale for vaccine hesitancy in Pakistani university students and support staff, and the impact of gender, age, and education on vaccination hesitancy. Methods A cross-sectional study was conducted with randomly selected university students and supportive staff. An anonymous questionnaire collected data covering socio-demographic characteristics, vaccination status, current beliefs, and past vaccination history. Responses were assessed using descriptive analysis (p-value estimation and correlation/regression) and machine learning models applied to classify individuals based on their acceptance or vaccine hesitancy behavior and then used to predict the important variables associated with vaccine hesitancy. Results The survey of 847 participants revealed that 43% were vaccinated and 57% were vaccine hesitant; of which 37%, 38%, and 25% had safety and efficacy concerns, were afraid of side effects, or exhibited other complacency behavior regarding vaccination, respectively. Non-university educated people were significantly more hesitant than university-educated people (p = 0.033). Vaccine refusal was significantly higher in students than in supportive staff (p = 0.01). Correlation analysis revealed a strong association between key independent variables (the fear of side effects, mistrusted information, and low perceived disease risk without vaccination) and vaccine acceptance (dependent variable), as revealed by linear regression and a Structure Model Equation. Machine learning classified vaccine-hesitant and vaccine-acceptant individuals with commutative accuracy of 96% and 97%, respectively, using random forest and logistic regression. Logistic regression identified five predictors for vaccine hesitancy: low perceived disease risk without vaccination, mistrusted information, the fear of side effects, occupation, and education. Conclusion Education campaigns that cover the safety, efficacy, and importance of vaccination are needed to increase vaccination take-up to protect from COVID-19 disease. " } { "@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/13-1241", "name": "Determinants of COVID-19 vaccine hesitancy in university students..." } } ] } Home Browse Determinants of COVID-19 vaccine hesitancy in university students... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Naseem A, Rasool F, Haashmi FK et al. Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine learning and statistical analysis [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :1241 ( https://doi.org/10.12688/f1000research.155659.1 ) 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 Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine learning and statistical analysis [version 1; peer review: awaiting peer review] Ayesha Naseem https://orcid.org/0000-0003-2198-7150 1 , Fatima Rasool 1 , Furqan K. Haashmi 1 , Muhammad Hamza Shoaib 2 , Wayne G. Carter https://orcid.org/0000-0003-1475-2476 3 Ayesha Naseem https://orcid.org/0000-0003-2198-7150 1 , Fatima Rasool 1 , [...] Furqan K. Haashmi 1 , Muhammad Hamza Shoaib 2 , Wayne G. Carter https://orcid.org/0000-0003-1475-2476 3 PUBLISHED 17 Oct 2024 Author details Author details 1 Punjab University College of Pharmacy, Faculty of Life Sciences, University of the Punjab, Lahore, Punjab, 05422, Pakistan 2 Allama Iqbal Medical College, Lahore, Punjab, 54700, Pakistan 3 Clinical Toxicology Research Group, School of Medicine, University of Nottingham Division of Medical Sciences and Graduate Entry Medicine, Derby, England, DE22 3DT, UK Ayesha Naseem Roles: Software, Writing – Original Draft Preparation Fatima Rasool Roles: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Furqan K. Haashmi Roles: Validation Muhammad Hamza Shoaib Roles: Writing – Original Draft Preparation Wayne G. Carter Roles: Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background Despite the availability of a vaccine to combat COVID-19 disease, vaccine hesitancy is still a major concern, notably in countries with developing healthcare systems such as Pakistan. Hence, this study considered the rationale for vaccine hesitancy in Pakistani university students and support staff, and the impact of gender, age, and education on vaccination hesitancy. Methods A cross-sectional study was conducted with randomly selected university students and supportive staff. An anonymous questionnaire collected data covering socio-demographic characteristics, vaccination status, current beliefs, and past vaccination history. Responses were assessed using descriptive analysis (p-value estimation and correlation/regression) and machine learning models applied to classify individuals based on their acceptance or vaccine hesitancy behavior and then used to predict the important variables associated with vaccine hesitancy. Results The survey of 847 participants revealed that 43% were vaccinated and 57% were vaccine hesitant; of which 37%, 38%, and 25% had safety and efficacy concerns, were afraid of side effects, or exhibited other complacency behavior regarding vaccination, respectively. Non-university educated people were significantly more hesitant than university-educated people (p = 0.033). Vaccine refusal was significantly higher in students than in supportive staff (p = 0.01). Correlation analysis revealed a strong association between key independent variables (the fear of side effects, mistrusted information, and low perceived disease risk without vaccination) and vaccine acceptance (dependent variable), as revealed by linear regression and a Structure Model Equation. Machine learning classified vaccine-hesitant and vaccine-acceptant individuals with commutative accuracy of 96% and 97%, respectively, using random forest and logistic regression. Logistic regression identified five predictors for vaccine hesitancy: low perceived disease risk without vaccination, mistrusted information, the fear of side effects, occupation, and education. Conclusion Education campaigns that cover the safety, efficacy, and importance of vaccination are needed to increase vaccination take-up to protect from COVID-19 disease. READ ALL READ LESS Keywords Anti-vaccination, COVID-19 disease, SARS-CoV-2, vaccine complacency, vaccine hesitancy, vaccine refusal Corresponding Author(s) Wayne G. Carter ( [email protected] ) Close Corresponding author: Wayne G. Carter Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Naseem A 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: Naseem A, Rasool F, Haashmi FK et al. Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine learning and statistical analysis [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :1241 ( https://doi.org/10.12688/f1000research.155659.1 ) First published: 17 Oct 2024, 13 :1241 ( https://doi.org/10.12688/f1000research.155659.1 ) Latest published: 17 Oct 2024, 13 :1241 ( https://doi.org/10.12688/f1000research.155659.1 ) Introduction COVID-19 disease is caused by infection with the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and typically presents clinically with respiratory disease, that can be fatal. 1 The SARS-CoV-2 virus outbreak was initiated in Wuhan, China in December 2019 but quickly spread worldwide. 1 , 2 In Pakistan, two confirmed cases were reported in Karachi on the 26 th of February 2020. 3 On 30 January 2020, the WHO declared that the coronavirus outbreak was a public health emergency of international concern and that it was a pandemic on the 11 th of March 2020. 4 The number of reported cases of COVID-19 disease is over 775.87 million with approaching 7 million deaths. 5 The development of SARS-CoV-2 antiviral treatments has been an ongoing challenge for scientists. 6 Although various antibiotics and antivirals have been recommended by the US Food and Drug Administration (FDA) on the basis of their antiviral properties, there is no definitive therapy that has yet been approved that is completely effective against SARS-CoV-2. 7 , 8 Assuming there is a willingness for uptake, vaccination is one of the preventive measures that can be employed in a pandemic situation as immunization is a highly successful health intervention often able to tackle severe and debilitating diseases within communities. 9 Eradicating diseases like smallpox, which cost about 100 million USD to eradicate, saves lives and also results in annual savings of roughly 1.35 billion USD. 9 Likewise, the completion of the polio eradication campaign is anticipated to save lives and an approximately 1.5 billion USD annual cost. 10 , 11 In early 2021, Pakistan launched its COVID-19 vaccination campaign, approximately one year after the first case. The government regulated the vaccination effort, procuring and distributing seven approved vaccines across Pakistan. Various partners assisted the government by promoting vaccine awareness, facilitating mobile vaccination, improving healthcare facilities, and collecting vaccination data. 12 There are differences in the rates of vaccination between countries. The analysis of the WHO/UNICEF Joint Reporting Form data from 2015-2017 has cited major causes of vaccine hesitancy among low-income countries as compared to middle-income countries. 13 According to a survey conducted in 23 countries, the vaccine acceptance ratio is 79.1%, up 5.2% in 2022. 14 However, vaccine hesitancy has increased in eight countries, ranging from the lowest vaccine hesitancy in the UK at 1.0% to the highest in South Africa at 21.1%. 14 In a study conducted in France, 80% of healthcare workers were willing to accept the COVID-19 vaccine, and this willingness grew as vaccination programs became more accessible. However, among those who were hesitant, the primary concern was the fear of adverse events. 15 A cross-sectional study of German healthcare workers revealed an overall COVID-19 vaccination acceptance of 91.7% in February 2021. 16 In a comprehensive search of 33 countries, the level of vaccine take-up differed between low-income and lower-income countries with India having the highest acceptance at 76.7%, and Egypt at the lowest rate of 42.6%. 17 Hence, despite the availability of a COVID-19 vaccine, avoidance and refusal of the vaccine are prevalent and collectively constitute vaccine hesitancy. 18 Vaccine hesitancy was defined by the WHO Strategic Advisory Group of Experts on Immunization as a behavior influenced by several factors and these include issues of confidence, complacency, and convenience. 13 Opposition to vaccination is not a new concept, 19 and would likely have existed from the time of Edward Jenner and the initiation of inoculation against smallpox in 1796. 19 After the polio outbreak in the 1940s, there were almost 15,000 cases of paralysis per year in the USA. 20 However, when the polio vaccination was developed and successfully administered, the paralysis ratio declined simultaneously. 21 Hence, the number of polio cases fell exponentially. 22 , 23 That polio remains and is still endemic in Pakistan likely relates to vaccination hesitancy. 23 In 2019, vaccine hesitancy was listed as a top 10 global health threat and the WHO promised to make this a priority for its organization and its partners. 24 Hence, the number of polio cases fell in the 1960s and remained less than 10 in the 1970s, and currently, polio has been eradicated from all over the world except for Nigeria, Afghanistan, and Pakistan. The Global Alliance for Vaccines and Immunizations ((Gavi) Geneva, Switzerland) suggests that for herd immunity against COVID-19 disease, approximately 60% of the population may need to be vaccinated, 25 although the numbers for other diseases such as measles require about 95% of a population to be vaccinated, and for polio this is approximately 80%. 25 , 26 Determinants of vaccine hesitancy according to the WHO ‘3Cs’ model include a lack of confidence (in vaccine safety and efficacy), complacency (low perceived disease risk and low self-efficacy), and convenience (of vaccine availability, affordability, and health literacy, of which mistrust of the benefit of vaccination and low perceived seriousness of COVID-19 disease can be main determinants of vaccine hesitancy. 13 , 27 Machine learning models serve as powerful tools that offer valuable insights into the medical field; providing an understanding of disease risk, clinical presentations/symptoms and treatment outcomes. 28 – 30 These models can evaluate potentially hidden patterns within large datasets and have been applied to the complex nature of classification tasks. 29 Recent studies have used different machine learning algorithms trained on social media data from platforms such as Twitter to detect anti-vaccination tweets. 31 Sentiment analysis techniques can be employed to predict vaccine hesitancy, and researchers have developed deep learning models and used transfer learning to identify anti-vaccination tweets. 32 COVID-19 vaccine hesitancy and acceptance can be analyzed in diverse datasets and provide an understanding of the factors that influence vaccine hesitancy and acceptance across cohorts by utilizing different machine learning models. 33 – 35 Random forest (RF) is an ensemble of classification and regression models that can analyze relatively large datasets by predicting outcomes based on the majority vote from trees and has been applied for the consideration of vaccine hesitancy. 32 , 36 Logistic regression can model the relationship between multiple independent parameters and a dependent categorical variable within the medical field and can also evaluate the relative contribution of each feature to the classification. 37 Previous studies have described vaccine hesitancy among the general population of Pakistan. 38 , 39 However, to date, there is a knowledge gap concerning the drivers for vaccine hesitancy amongst university students and support staff in Pakistan. Hence, the primary goal of this study is to consider the factors influencing COVID-19 vaccine hesitancy in these cohorts and evaluate the impact of gender, age, and education on vaccine hesitancy by utilizing statistical tools. The secondary objective is to identify how accurately machine learning models can classify individuals in the dataset based on their acceptance or hesitancy towards vaccination and extract the key features associated with vaccine hesitancy. Methods Study design and setting A cross-sectional study was conducted from June to November 2021 to evaluate vaccine hesitancy among students and workers of different departments of a public sector university in Lahore, Pakistan. A comprehensive self-administered questionnaire was designed with the help of the Health Belief Model as a theoretical framework, 40 as per study objectives, to be completed by university students and support staff, followed by a short interview by the lead researcher. The survey included undergraduate students, postgraduate students, technical staff, laboratory attendants, management staff, security workers, and other support staff of the university. The staff from all of these socio-demographic backgrounds were included to assist with the generation of a broader representation of possible trends towards vaccination take-up. The questionnaire content was considered and approved by the University of the Punjab Institutional Ethics Review Board (IRB), with approval number D/35/FIMS on the 21 st of September, 2021. Written informed consent was obtained from all subjects involved in the study. The participants were provided with detailed information regarding the purpose, procedures, potential risks, and benefits of the study, and their rights to withdraw at any stage without any consequences were emphasized. No participants under the age of 18 were included in the study, and all participants had the opportunity to ask questions and receive answers before giving consent. The use of human participants for the study adhered to the Declaration of Helsinki https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ . Written informed consent was obtained from all participants prior to their involvement in the study. The consent form stated the following: “I am a Pharm-D student at the University College of Pharmacy, Punjab University. As pharmacy students, we are conducting a project on COVID-19 vaccination. Ethical approval for this study has been granted by the committee of the University College of Pharmacy, Punjab University. The primary objective of our survey is to assess vaccine acceptance and rejection behaviors, along with their potential determinants, through a cross-sectional study among university students and staff. You are invited to participate in this observational study. We assure you that your personal information will remain confidential. If you have any questions, please feel free to ask.” The overall process is shown in Figure 1 . Figure 1. Flowchart of the project methodology. Study population The questionnaire was distributed to 1000 people based at the university using convenience sampling. An effort was made to include people from all administrative, lower-level, and academic departments. Only the participants who were greater than 18 years of age and were willing to participate were included in this study. A total of 847 respondents completed the survey. Data collection tool A brief self-administered questionnaire was developed based on the aim of the study. The questionnaire was comprised of three sections. The first section contained socio-demographic information such as gender, age, education, and occupation. The second section had questions covering knowledge about vaccines and the final section contained questions on vaccine hesitancy. The questionnaire content was considered and approved by the Punjab University Institutional Ethics Review Board, study number D/35/FIMS on the 21 st of September 2021. The names of all available vaccines were included in the questionnaire to detect any potential brand preferences for the vaccines by the surveyed participants. Data collection procedure Both the web-based and paper forms of the questionnaire were prepared and distributed to the participants. The web-version was shared with students as a more convenient way to reach those who stayed at home during the time of social distancing and may have been more familiar with web-based surveys. The paper form was provided for university support staff as some may not have been familiar with web-based surveys using smartphones. A self-explanatory description was included in the questionnaire to cover the aims and objectives of the study. For participants that used the paper-based questionnaire, an on-the-spot explanation and help were also provided to ensure an understanding of each aspect of the questionnaire and to assist with the inputting of responses. Written consent was taken from all willing respondents and their personal information was kept confidential. Data analysis Statistical analysis Study data were analyzed using SPSS Statistics (version 26) and SPSS graphic (version 23) https://www.ibm.com/products/spss-statistics and Microsoft Excel 2010 https://www.microsoft.com/en-us/microsoft-365/excel . Descriptive analysis of data was obtained by percentage and frequency. A Chi-square test was applied to check the association between socio-demographic parameters (independent variable) with vaccine refusal/acceptance (dependent variable). Univariate analysis was performed to estimate the odds ratio for the variables affecting vaccine hesitancy. Linear regression coefficient analysis was employed to estimate the impact of individual independent factors on vaccine hesitancy and a graph was plotted using the Seaborn library to visualize the results. The percentages of individual hesitancy factors and their significance were quantified with a p -value set at 0.05. A Structure Equation Model (SEM) was employed to estimate and represent the hypothesized relationship between the variables in a structural framework. Machine learning model Machine learning models were employed to consider how well they could classify subjects with vaccine hesitancy and vaccine acceptance in our dataset, as well as to identify the number of variables contributing to vaccine hesitancy. In this machine learning workflow, the data were randomly split into two portions: 70% for training and 30% for testing, with 5-fold cross-validation. Machine learning models were then applied to train our models. The dataset was fitted to two different machine learning approaches using libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn in the Python programming language (Python 3). Random forest and logistic regression algorithms were used to evaluate this classification problem. Random forest (RF) is an ensemble of classification and regression models that can accurately analyze vaccine hesitancy in large datasets by predicting outcomes based on the majority vote of these trees. Logistic regression is a linear model used to predict the probabilities of different outcomes (e.g., vaccine hesitancy or acceptance) as a function of independent parameters (e.g., demographics and vaccine hesitancy parameters). The outcome of the linear combination of features is mapped to a probability value between 0 and 1 using the logistic function. Metrics for evaluating performance Accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC curve) were used to validate the performance of each model. Accuracy refers to the proportion of correctly identified hesitant individuals out of all predictions. A higher accuracy score indicates that the model predicts correctly, while a lower accuracy suggests that the model is less effective in its predictions. Another crucial metric is precision, or positive predictive value, which shows the percentage of accurately predicted vaccine-hesitant individuals relative to all predictions. The percentage of accurately anticipated vaccine-hesitant individuals over all actual vaccine-hesitant persons is called recall, often referred to as sensitivity. F1-score is used as the primary metric, as it balances precision and recall for the classification model. The range of the F1 score is from 0 to 1. The AUC is a plot between true-positive and false-positive rates. (1) Accuracy = True positive + True negative Total number of predictions (2) Precision = True positive True positive + True negative (3) Recall = True positive True positive + F alse negative (4) F 1 score = 2 × Precision × Recall Precision + Recall Results Socio-demographics The socio-demographic data from the survey is presented in Table 1 . Out of 847 people who participated in the survey, 638 (75%) were students of undergraduate or postgraduate level, 209 (25%) were supportive staff including clerks, management staff, technical staff, lab attendants, shopkeepers, and guards. The overall response rate was 84.7% and the majority of the responders were female students: 582 (68.7%) females and 265 (31.3%) males, since more female participants responded to the survey. Among participants, 701 (82.8%) participants were ≤30 years of age, while 146 (17.2%) were >30 years. In terms of educational status, 711 (84%) participants were university educated (undergraduate or postgraduate programs) while 136 (16%) were non-university educated. According to occupation, 638 (75%) were students and 209 (25%) were supportive staff. Out of the 847 respondents, 363 (43%) were vaccinated, while 484 (57%) remained thus far hesitant. Hence, approximately half of the study population had not yet adopted the COVID-19 vaccine as shown in Table 1 . Table 1. Descriptive studies and bivariate analysis of the socio-demographic parameters associated with COVID-19 vaccine status. Characteristics Parameters Frequency Percentage Vaccination status P-value Odd Ratio (CI:95%) Lower value Upper value No (n = 484) Yes (n = 363) Gender Female 582 68.7 329 (38.8%) 253 (29.9%) 0.593 0.923 0.688 1.239 Male 265 31.3 155 (18.3%) 110 (12.9%) Age ≤30 years 701 82.8 414 (48.9%) 287 (33.9%) 0.14 1.566 1.095 2.240 >30 years 146 17.2 70 (8.3%) 76 (9.0%) Education Non-university educated 136 16.1 89 (10.5%) 47 (5.6%) 0.033 * 1.515 1.033 2.222 University educated 711 83.9 395 (46.5%) 316 (37.3%) Occupation Students 638 75.3 385 (45.5%) 253 (29.9%) 0.01 * 1.691 1.235 2.316 Supportive staff 209 24.7 99 (11.7%) 110 (13.0%) * p < 0.05. Among the 582 females, 329 (38.8%) were not yet vaccinated, 253 (29.9%) were fully vaccinated, and for the 265 males, 155 (18.3%) were not vaccinated, while 110 (12.9%) were vaccinated. Comparatively, the hesitancy associated with vaccine take-up was more prevalent in females than in males ( p = 0.593) but this did not reach significance. Approximately 49% of the younger cohort (≤30 years) was not yet vaccinated, while approximately 34% had been. Hence, the younger cohort were more resistant towards vaccination when compared to the acceptance from the older (and potentially higher risk) cohort of >30 years of age. Hence, respondents within the 31-65 age range were more accepting of vaccination ( p = 0.14). With regard to the levels of education, approximately 47% of the cohort that were university educated remained unvaccinated, whereas 37% were vaccinated. By comparison, a relatively higher percentage (11%) of the cohort that were non-university educated were not vaccinated compared to approximately 6% that were ( p = 0.03). Approximately 46% of the cohort that were students were not vaccinated whereas 30% were. This was in comparison with support staff in which the proportion of vaccinated was 13%, whereas approximately 12% were not vaccinated ( p = 0.01). Hence, occupation has a significant impact on the number of vaccinated individuals and associated vaccine hesitancy within the cohort, as shown in Table 1 . The prominent trend amongst supportive staff of the university indicated that being a part of these cohorts had a positive impact on vaccine acceptance ( p < 0.05). Despite the accessibility of the COVID-19 vaccine, 57.1% of the respondents had not been vaccinated ( Table 1 ) and this was due to the following reasons: 13% did not get the vaccine due to a busy routine, 37% of people had trust issues (safety and efficacy concerns) regarding vaccines; approximately 38% were afraid of side effects after vaccination; approximately 7% thought that they may have pre-formed antibodies enough to mitigate their future risk; approximately 2% did not get vaccinated because of pregnancy, and approximately 3% of the cohort had other co-morbidities (such as diabetes, hypertension, and allergic issues etc) and needed further consultation from their physicians, as shown in Figure 2 . Figure 2. Reasons for not getting vaccinated against COVID-19 disease. Correlation analysis Figure 3 indicates the correlation of vaccine acceptance (dependent variable) with demographics and beliefs of an individual (independent variables), in which “Q3: Do you think you need to boost your immunity?” is positively correlated with vaccine acceptance at 94%. Similarly, Q1 “Do you have any trust issues (safety and efficacy concerns) regarding COVID-19 vaccines?” and “Q2: Are you afraid of side effects after vaccination?” negatively influence vaccine acceptance at 59% and 38%, respectively. In terms of demographics, only education showed an 11% correlation with vaccine acceptance as compared to occupation, gender and age variables are shown in Figure 3 . Figure 3. Correlation plot between all independent variables and the dependent variable, vaccine acceptance. Key variables were further evaluated by using a Structure Model Equation to consider the relationship between vaccine hesitancy and an individual’s beliefs. Several latent variables, such as trust in healthcare providers, perceived risk and efficacy and observed variables, social influence (future risk estimation) and past vaccination status represent the indicators to evaluate vaccine hesitancy ( Figure 4 ). The arrows in Figure 4 indicate the relationship of these variables to vaccine hesitancy and ‘e’ refers to the error terms and these are represented as small ovals attached to the variables. Results indicated that trust issues and safety and efficacy concerns had a 0.88 (88%) and 0.57 (57%) positive impact on hesitancy, respectively. The future risk of COVID-19 disease and past vaccination status were 0.67 (67%) and 0.13 (13%) negative impact factors on vaccine hesitancy, respectively. Figure 4. A Structural Equation Model (SEM) diagram depicting factors affecting vaccine hesitancy. The beliefs, concerns of vaccine hesitancy, and past vaccination status were considered and 535 (63.2%) responders had safety and efficacy concerns regarding the vaccine, and 458 (54.1%) of the cohort were concerned with side effects of the vaccine. Furthermore, 460 (54.3%) of responders believed in their natural immunity and 160 (19%) people had not yet been vaccinated. The details of trust issues, safety and efficacy concerns, future risk perception, and past vaccination status are covered in Table 2 . Table 2. Beliefs of participants towards complacency, confidence in the COVID-19 vaccine, and its influence on vaccine hesitancy. Parameters Questions Responses P value Odds Ratio 95% (CI) No Yes lower higher Trust issues Do you have any trust issues (safety and efficacy concerns) regarding COVID-19 vaccines? 312 (36.8%) 535 (63.2%) <0.01 * 0.060 0.042 0.086 Safety and efficacy concerns Are you afraid of side effects after vaccination? 389 (45.9%) 458 (54.1%) <0.01 * 0.199 0.149 0.268 Future risk estimation Do you think you need to boost your immunity? 460 (54.3%) 387 (45.7%) <0.01 * 16.125 10.945 23.756 Past vaccination status Have you got vaccinated before? 160 (18.9%) 687 (81.1%) <0.01 * 1.841 1.276 2.656 * Denotes a significant result with a p value <0.05. Machine learning models We evaluated the performance of our machine learning models in classifying individuals based on their acceptance or hesitancy towards vaccination. The accuracy score of random forest was 96% and logistic regression was 97%. To validate this further, precision, recall and F1-score were considered. Precision measures the proportion of true positive prediction (vaccine hesitant people) among all positive predictions made by the model. The precision of both models was high (>90%). Recall (sensitivity) is the proportion of true positive prediction among all actual positive values. These values were close to 1 indicative that both models can identify the majority of vaccine hesitant and non-hesitant people in the dataset. The F1-score provided a balance between the two metrics, precision and recall. Both models had high F1-scores for both classes, consistent with a good balance between precision and recall. Overall, a comparative analysis of both models indicates that logistic regression can perform better than random forest in classifying the individual based on vaccine hesitancy profile in terms of precision and accuracy as shown in Table 3 . Table 3. Evaluation parameters for the machine learning models. Parameters Outcomes Machine learning models Random Forest Logistic Regression Accuracy score 0.96 0.97 Precision (Sensitivity) 0 (Vaccine acceptance) 0.99 1 1 (Vaccine hesitant) 0.93 0.93 Recall 0 (Vaccine acceptance) 0.95 0.95 1 (Vaccine hesitant) 0.99 1 F1-Score 0 (Vaccine acceptance) 0.96 0.97 1 (Vaccine hesitant) 0.96 0.96 The AUC-ROC for the random forest model is 0.98 and 0.99 for the logistic regression model, as shown in Figure 5 . Figure 5. AUC-ROC plot of random forest (left panel) and logistic regression (right panel). Confusion matrix Both the random forest and logistic regression models correctly predicted non-hesitancy (actual value 0) 96% of the time (true negatives) but incorrectly predicted hesitancy (actual value 1) 5% of the time (false negatives) in both cases. For individuals who are hesitant (actual value 1), the models correctly predicted hesitancy 68% of the time (true positives), but the logistic regression model achieved a slightly higher accuracy rate of 69%. Additionally, both models incorrectly predicted non-hesitancy 1% of the time (false positives), but the logistic regression model did not make any incorrect predictions of non-hesitancy as shown in Figure 6 . Figure 6. Confusion matrix of random forest (left panel) and logistic regression (right panel) models. Features importance In logistic regression, feature extraction involves assigning weights to important features to calculate the probability of the predicted outcomes. These weights indicate the impact and relationship of these variables to the predicted outcome. If future risk estimation is higher in people, it indicates more vaccine hesitancy, and similarly, the occupation and education of the individual also influence vaccine hesitancy. However, feature importance predicts that a person with trust issues regarding vaccine safety and efficacy, and who is afraid of side effects, may be more likely to be vaccine hesitant, as shown in Figure 7 . Figure 7. Important variables of hesitancy plot using machine learning model. Discussion Immunization is a useful strategy to limit the spread and health impact of COVID-19 disease. However, a lack of engagement with a vaccine program is still a problem in some countries such as Pakistan. A major obstacle to vaccine acceptance and the establishment of a herd immunity threshold is a lack of confidence in vaccines, particularly mistrust regarding the safety and effectiveness of the COVID-19 vaccine. 41 Hence, this study aimed to determine the significance of demographic characteristics (gender, age, education, and occupation) in relation to vaccine acceptance using a Chi-square test. The study was designed to determine major contributing factors towards vaccine hesitancy by statistical and machine learning models. The four factors studied in this regard were gender, age, level of education, and occupation. The results revealed that gender and age did not significantly impact vaccine acceptance, while education and occupation were significantly associated (p < 0.05) with vaccine acceptance. Furthermore, individual vaccine hesitancy was considered within the context of the WHO ‘3Cs’ determinants of Confidence, Complacency, and Convenience. 13 By employing machine learning algorithms on our data, we were able to predict vaccine hesitancy and acceptance with 96% and 97% accuracy using random forest and logistic regression analyses, respectively. Our study revealed that low perceived disease risk without vaccination, mistrusted information, the fear of side effects, and occupation and education were the strong predictors of vaccine hesitancy by statistical and machine learning driven data. Previous studies have also identified vaccine hesitancy in high-income countries with high accuracy, achieving 82% sensitivity and 79–82% specificity using different machine learning models. A predictive model of vaccine hesitancy in America showed that gradient boosting and a random forest algorithm can correctly classify a vaccine acceptant individual with 97% accuracy, and a vaccine hesitant individual with 72% accuracy, and suggested that the lack of interest in vaccination is the key predictor when using the Scientific Advisory Group on Emergencies (SAGE) vaccine hesitancy parameters. 34 Zhou et al. (2015) developed a supervised classifier to identify anti-vaccine tweets using a random sample that utilized a support vector machine (SVM) method with 88.6% accuracy. 42 A systematic search covering low- and lower-income countries suggests that the average vaccine acceptance ratio was 58.5% with the highest acceptance in India (76.7%) and the lowest in Egypt (42.6%). 17 A similar survey of lower-income countries revealed that Burkina Faso and Pakistan have the lowest acceptance rates at 66.5%, 43 with Bangladesh similar with 66.6% of people accepting vaccination. 44 Our study, albeit with a smaller sample size, had an acceptance rate of 43% which is similar to the Egyptian ratio. 17 , 43 Early in the pandemic, the vulnerability of older people to fatal COVID-19 disease was publicized due to a potentially weakened immune system and the presence of other comorbidities. 45 Herein, the older age group (>30 years of age) did have a higher vaccination take-up than the younger group (<30 years), although this difference did not have a significant impact on vaccine hesitancy (p = 0.14) ( Table 1 ). Vaccine hesitancy trends in both genders were similar and the influence of gender on vaccine hesitancy was not statistically significant (p = 0.593). In contrast, education level had a significant impact on vaccination status (p = 0.033). A cross-sectional study on vaccination hesitancy in Egypt reported that, despite having relevant medical education, a number of students refused to get vaccinated due to concerns about side effects and misconceptions about vaccines. 46 Similarly, vaccine hesitancy behavior was observed among Chinese medical students. 47 In our studies, despite students having different educational backgrounds, their opposition to getting vaccinated remains consistent. However, there is a higher willingness among supportive staff to receive the vaccination (p = 0.033) as detailed in Table 1 . The second most prominent fear expressed in our findings was safety and efficacy concerns related to COVID-19 vaccines. Previous studies have reported that conspiracy beliefs and misinformation are linked to vaccine hesitancy and these can be propagated by various streams of media such as social-, electronic- and print-media. 48 Individuals are more likely to get vaccinated if they receive vaccination information from official health sources rather than unofficial ones. 49 Public perception about vaccination can be influenced by healthcare professionals and people planning to get vaccinated by trusted doctors for information regarding COVID-19. 50 Another determinant of vaccine hesitancy is confidence in the vaccine that relates to its safety and efficacy. Approximately 38% of our study participants were afraid of the side effects of vaccination, which can be a key discriminating variable for past vaccination refusal. 51 Furthermore, mistrust in the benefits of vaccines also influences vaccine confidence. 52 Another contributing factor to vaccine reluctance was complacency and a decreased sense of the severity of the COVID-19 virus. This finding indicates the need for a strategic plan of public health messaging targeting that addresses this issue. Our study findings indicate that past vaccination refusal is a key parameter in COVID-19 vaccination hesitancy ( Table 2 ). Hence, past vaccination history can also contribute to vaccine hesitancy, with younger people’s previous vaccination history indicative of whether they will be less resistant to new vaccines. People who previously had positive attitudes toward vaccination practices had more confidence in immunization against COVID-19 disease, and this is consistent with other studies conducted in the USA and Italy which showed that previous uptake of influenza vaccine was a predictor of COVID-19 vaccination intent. 53 , 54 In addition, individuals who had never received any vaccines in their lifetime displayed a higher degree of vaccine hesitancy ( Table 2 ). A number of suggestions and recommendations could be adopted to try and mitigate some of the concerns that relate to vaccine hesitancy ( Table 2 ). A counselling facility at a pharmacy or healthcare center could promote contact with a health professional to assist with questions and concerns about the safety and efficacy of vaccines. Educational campaigns at college and university levels will also help to provide authentic and relevant information to students and consideration could be given to check and monitor their exposure to anti-vaccination propaganda that can be spread on social media. Vaccine hesitancy may be better addressed by the continuous monitoring of vaccine safety, addressing public concerns through research, providing compensation for adverse events, and introducing specific training for healthcare professionals to counter anti-vaccination misinformation. 55 A spread of information by teachers, motivational speakers, religious scholars and celebrities could provide validation of appropriate and established facts regarding vaccination since vaccine take-up by others can influence vaccination intention. 56 Printed sources and media advertisements should be meaningful and appropriate. Countries such as Israel (and the UK) have successfully used public awareness campaigns to promote vaccination and combat vaccine hesitancy. 57 A campaign of parental education may improve vaccination in children and young adults and public confidence will improve with post-vaccination follow-up and publicity regarding pharmacovigilance. A collaboration with employers to organize workplace vaccination drives, making it convenient for employees to get vaccinated during working hours may be fruitful as this approach has been successful for influenza vaccination. 58 Ultimately, with an increase in the number of vaccinated people in Pakistan, closer and potentially more rapid herd immunity could be achieved and the mortality and morbidity arising from SARS-CoV-2 infection will likely decline. Conclusions This study considered the hesitancy to the COVID-19 vaccine in a Pakistani cohort and how this may be impacted by socio-demographics. By utilizing both conventional statistical methods and machine learning models, this research revealed that trust issues, safety and efficacy concerns, future risk perception, and education and occupation are contributing factors and key drivers of vaccine hesitancy. These findings suggest that possible interventional educational programs and counselling by healthcare providers regarding the safety and efficacy of vaccines may represent possible approaches to mitigate vaccine hesitancy. Author’s contributions Conceptualization, Methodology: FR; Data curation, Writing- Original draft preparation: AN and MHS; Visualization, Investigation: FR; Supervision: FR; Software: AN; Validation: FR and FKH; Writing-Reviewing and Editing: FR and WGC. All authors have read and agreed to the published version of the manuscript. Ethics and consent The questionnaire content was considered and approved by the University of the Punjab Institutional Ethics Review Board (IRB), with approval number D/35/FIMS on the 21 st of September, 2021. Written informed consent was obtained from all subjects involved in the study. The participants were provided with detailed information regarding the purpose, procedures, potential risks, and benefits of the study, and their rights to withdraw at any stage without any consequences were emphasized. No participants under the age of 18 were included in the study, and all participants had the opportunity to ask questions and receive answers before giving consent. The use of human participants for the study adhered to the Declaration of Helsinki https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/ . Written informed consent was obtained from all participants prior to their involvement in the study. The consent form stated the following: “I am a Pharm-D student at the University College of Pharmacy, Punjab University. As pharmacy students, we are conducting a project on COVID-19 vaccination. Ethical approval for this study has been granted by the committee of the University College of Pharmacy, Punjab University. The primary objective of our survey is to assess vaccine acceptance and rejection behaviors, along with their potential determinants, through a cross-sectional study among university students and staff. You are invited to participate in this observational study. We assure you that your personal information will remain confidential. If you have any questions, please feel free to ask.” Data availability Underlying data 1. Figshare. Survey-based data on “Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: a machine learning and statistical analysis”. https://doi.org/10.6084/m9.figshare.26999635.v1 . 59 This project contains following underlying data: • Vaccination hesitancy dataset.xlsx. 2. Figshare. Vaccine hesitancy: machine learning model. https://doi.org/10.6084/m9.figshare.26776426.v1 . 60 This project contains following underlying data: • Machine learning models.html Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Extended data 3. Figshare: Supplemental files. https://doi.org/10.6084/m9.figshare.26736583.v1 . 61 This project contains following extended data: • Supplemental files.pdf. • Ethical approval.jpg Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Reporting guidelines Figshare: STROBE checklist for “Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: a machine learning and statistical analysis”. https://doi.org/10.6084/m9.figshare.26800840.v1 . 62 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 all the study participants for the provision of the study data. References 1. Wu D, Wu T, Liu Q, et al. : The SARS-CoV-2 outbreak: what we know. Int. J. Infect. Dis. 2020; 94 : 44–48. PubMed Abstract | Publisher Full Text | Free Full Text 2. Guo Q, He Z: Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence. Environ. Sci. Pollut. Res. 2021 Mar 28; 28 : 11672–11682. PubMed Abstract | Publisher Full Text | Free Full Text 3. Noreen N, Dil S, Niazi SUK, et al. : Coronavirus disease (COVID-19) pandemic and Pakistan; limitations and gaps. Global Biosecurity. 2020 Sep 27; 1 (3): 1–11. Publisher Full Text 4. Cucinotta D, Vanelli M: WHO declares COVID-19 a pandemic. Acta Biomed. 2020; 91 (1): 157–160. PubMed Abstract | Publisher Full Text | Free Full Text 5. World Health Organization: COVID-19 dashboard 2024.[cited 2024 Sep 11]. Reference Source 6. Edwards AM, Baric RS, Saphire EO, et al. : Stopping pandemics before they start: Lessons learned from SARS-CoV-2. Science. 2022 Mar 11; 375 (6585): 1133–1139. PubMed Abstract | Publisher Full Text 7. Coronavirus treatment acceleration program (CTAP). U.S. FDA; 2023 [cited 2024 Sep 11]. Reference Source 8. Nainwal LM, Suman.: FDA Coronavirus Treatment Acceleration Program: approved drugs and those in clinical trials. Coronavirus. Drug Des. Discov. 2022 Jan 1; 249–264. Publisher Full Text 9. Doherty M, Buchy P, Standaert B, et al. : Vaccine impact: Benefits for human health. Vaccine. 2016 Dec 20; 34 (52): 6707–6714. Publisher Full Text 10. Barrett S: Eradication versus control: the economics of global infectious disease policies. Bull. World Health Organ. 2004. 2004 Sep; 82 (9): 683–688. PubMed Abstract Reference Source 11. Bloom DE, Canning D, Weston M: The value of vaccination. Fighting the diseases of poverty. 2017 Jul 5; pp. 214–238. Publisher Full Text 12. Pakistan N: SUCCESS STORY: COVID-19 VACCINATION DRIVE IN PAKISTAN.2022 [cited 2024 Sep 11]. Reference Source 13. MacDonald NE: Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015 Aug 14; 33 (34): 4161–4164. PubMed Abstract | Publisher Full Text 14. Lazarus JV, Wyka K, White TM, et al. : A survey of COVID-19 vaccine acceptance across 23 countries in 2022. Nat. Med. 2023 Feb; 29 (2): 366–375. PubMed Abstract | Publisher Full Text 15. Janssen C, Maillard A, Bodelet C, et al. : Hesitancy towards COVID-19 Vaccination among Healthcare Workers: A Multi-Centric Survey in France. Vaccines. 2021 May 22; 9 (6): 547. PubMed Abstract | Publisher Full Text | Free Full Text 16. Holzmann-Littig C, Braunisch MC, Kranke P, et al. : COVID-19 vaccination acceptance and hesitancy among healthcare workers in Germany. Vaccines. 2021 Jul 12; 9 (7): 777. PubMed Abstract | Publisher Full Text | Free Full Text 17. Patwary MM, Alam MA, Bardhan M, et al. : COVID-19 vaccine acceptance among low-and lower-middle-income countries: a rapid systematic review and meta-analysis. Vaccines. 2022; 10 : 427. Publisher Full Text 18. Succi RC: Vaccine refusal - what we need to know. J. Pediatr. 2018 Apr 12; 94 (6): 574–581. PubMed Abstract | Publisher Full Text 19. Shen AK: Finding a way to address a wicked problem: vaccines, vaccination, and a shared understanding. Hum. Vaccin. Immunother. 2020 May 3 2020/05/03; 16 (5): 1030–1033. PubMed Abstract | Publisher Full Text | Free Full Text 20. Modlin J: Poliomyelitis and poliovirus immunization. Human enterovirus infections. 1995 Apr 27; pp. 193–220. Publisher Full Text 21. Groce NE, Banks LM, Stein MA: Surviving polio in a post-polio world. Soc. Sci. Med. 2014 Apr 1 2014/04/01/; 107 : 171–178. PubMed Abstract | Publisher Full Text 22. Ahmad T, Khan M, Musa TH, et al. : Polio vaccination campaign in Pakistan: a step towards eradication or still a challenge in hand? Hum. Vaccin. Immunother. 2020 Jun 2; 16 (6): 1444–1445. PubMed Abstract | Publisher Full Text | Free Full Text 23. Rahim S, Ahmad Z, Abdul-Ghafar J: The polio vaccination story of Pakistan. Vaccine. 2022; 40 (3): 397–402. PubMed Abstract | Publisher Full Text 24. World Health Organization: Ten threats to global health in 2019. World Health Organization; 2019 [cited 2024 21-08-2024]. Reference Source 25. Joi P: What is herd immunity?2020 [cited 2024 Sep 11]. Reference Source 26. Coronavirus disease (COVID-19): Herd immunity, lockdowns and COVID-19. World Health Organization; 2020 [cited 2024 Sep 11]. Reference Source 27. Gerretsen P, Kim J, Caravaggio F, et al. : Individual determinants of COVID-19 vaccine hesitancy. PLoS One. 2021 Nov 17; 16 (11): e0258462. PubMed Abstract | Publisher Full Text | Free Full Text 28. Khalilia M, Chakraborty S, Popescu M: Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inform. Decis. Mak. 2011 Dec; 11 : 1–3. PubMed Abstract | Publisher Full Text | Free Full Text 29. Van der Schaar M, Alaa AM, Floto A, et al. : How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach. Learn. 2021 Jan; 110 : 1–4. PubMed Abstract | Publisher Full Text | Free Full Text 30. Ahamad MM, Aktar S, Rashed-Al-Mahfuz M, et al. : A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Expert Syst. Appl. 2020 Dec 1; 160 : 113661. PubMed Abstract | Publisher Full Text | Free Full Text 31. To QG, To KG, Huynh V-AN, et al. : Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health. 2021 Apr 12; 18 (8): 4069. PubMed Abstract | Publisher Full Text | Free Full Text 32. Bar-Lev S, Reichman S, Barnett-Itzhaki Z: Prediction of vaccine hesitancy based on social media traffic among Israeli parents using machine learning strategies. Isr. J. Health Policy Res. 2021 Dec; 10 : 48–49. PubMed Abstract | Publisher Full Text | Free Full Text 33. Lincoln TM, Schlier B, Strakeljahn F, et al. : Taking a machine learning approach to optimize prediction of vaccine hesitancy in high income countries. Sci. Rep. 2022; 12 (1): 2055. PubMed Abstract | Publisher Full Text | Free Full Text 34. Mewhirter J, Sagir M, Sanders R: Towards a predictive model of COVID-19 vaccine hesitancy among American adults. Vaccine. 2022; 40 (12): 1783–1789. PubMed Abstract | Publisher Full Text | Free Full Text 35. Hafizh M, Badri Y, Mahmud S, et al. : COVID-19 vaccine willingness and hesitancy among residents in Qatar: a quantitative analysis based on machine learning. J. Hum. Behav. Soc. Environ. 2022; 32 (7): 899–922. Publisher Full Text 36. Gregorutti B, Michel B, Saint-Pierre P: Correlation and variable importance in random forests. Stat. Comput. 2017 May; 27 : 659–678. Publisher Full Text 37. Boateng EY, Abaye DA: A review of the logistic regression model with emphasis on medical research. Journal of Data Analysis Information Processing. 2019 Oct 12; 07 (04): 190–207. Publisher Full Text 38. Qazi SH, Masoud S, Usmani MA: Vaccine hesitancy: acceptance of COVID-19 vaccine in Pakistan. Clin. Exp. Vaccine Res. 2023; 12 (3): 209–215. PubMed Abstract | Publisher Full Text | Free Full Text 39. Khan AA, Abdullah M, Aliani R, et al. : COVID-19 vaccine hesitancy and attitudes in Pakistan: a cross-sectional phone survey of major urban cities. BMC Public Health. 2023 Jun 9; 23 (1): 1112. PubMed Abstract | Publisher Full Text | Free Full Text 40. Champion VL, Skinner CS: The health belief model. “Health behavior and health education: Theory, research, and practice”.2008; 4 : 45–65. Reference Source 41. Palamenghi L, Barello S, Boccia S, et al. : Mistrust in biomedical research and vaccine hesitancy: the forefront challenge in the battle against COVID-19 in Italy. Eur. J. Epidemiol. 2020 Aug; 35 : 785–788. PubMed Abstract | Publisher Full Text | Free Full Text 42. Zhou X, Coiera E, Tsafnat G, et al. : Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. MEDINFO 2015: eHealth-enabled Health. ISO Press; 2015; pp. 761–765. Publisher Full Text 43. Solís Arce JS, Warren SS, Meriggi NF, et al. : COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries. Nat. Med. 2021 Aug; 27 (8): 1385–1394. PubMed Abstract | Publisher Full Text | Free Full Text 44. Mahmud S, Mohsin M, Khan IA, et al. : Knowledge, beliefs, attitudes and perceived risk about COVID-19 vaccine and determinants of COVID-19 vaccine acceptance in Bangladesh. PLoS One. 2021 Sep 9; 16 (9): e0257096. PubMed Abstract | Publisher Full Text | Free Full Text 45. Mueller AL, McNamara MS, Sinclair DA: Why does COVID-19 disproportionately affect older people? Aging (Albany NY). 2020 May 5; 12 (10): 9959–9981. PubMed Abstract | Publisher Full Text | Free Full Text 46. Saied SM, Saied EM, Kabbash IA, et al. : Vaccine hesitancy: Beliefs and barriers associated with COVID-19 vaccination among Egyptian medical students. J. Med. Virol. 2021 Jul; 93 (7): 4280–4291. PubMed Abstract | Publisher Full Text | Free Full Text 47. Gao X, He W, Li H, et al. : COVID-19 Vaccine Hesitancy Among Medical Students: The Next COVID-19 Challenge in Wuhan, China. Disaster Med. Public Health Prep. 2023 Jan; 17 : e46. PubMed Abstract | Publisher Full Text | Free Full Text 48. Romer D, Jamieson KH: Conspiracy theories as barriers to controlling the spread of COVID-19 in the US. Soc. Sci. Med. 2020 Oct; 263 : 113356. PubMed Abstract | Publisher Full Text | Free Full Text 49. Bish A, Yardley L, Nicoll A, et al. : Factors associated with uptake of vaccination against pandemic influenza: a systematic review. Vaccine. 2011 Sep 2; 29 (38): 6472–6484. PubMed Abstract | Publisher Full Text 50. Earnshaw VA, Eaton LA, Kalichman SC, et al. : COVID-19 conspiracy beliefs, health behaviors, and policy support. Transl. Behav. Med. 2020 Aug; 10 (4): 850–856. PubMed Abstract | Publisher Full Text | Free Full Text 51. Reno C, Maietti E, Fantini MP, et al. : Enhancing COVID-19 Vaccines Acceptance: Results from a Survey on Vaccine Hesitancy in Northern Italy. Vaccines. 2021 Apr 13; 9 (4): 378. PubMed Abstract | Publisher Full Text | Free Full Text 52. Godasi GR, Donthu RK, Mohammed AS, et al. : Attitude towards COVID-19 vaccine among the general public in south India: A cross sectional study. Arch. Ment. Health. 2021; 22 (1): 28–35. Publisher Full Text 53. Ruiz JB, Bell RA: Predictors of intention to vaccinate against COVID-19: Results of a nationwide survey. Vaccine. 2021; 39 (7): 1080–1086. PubMed Abstract | Publisher Full Text | Free Full Text 54. Caserotti M, Girardi P, Rubaltelli E, et al. : Associations of COVID-19 risk perception with vaccine hesitancy over time for Italian residents. Soc. Sci. Med. 2021; 272 : 113688. PubMed Abstract | Publisher Full Text | Free Full Text 55. D’Errico S, Turillazzi E, Zanon M, et al. : The model of “informed refusal” for vaccination: How to fight against anti-vaccinationist misinformation without disregarding the principle of self-determination. Vaccine. 2021; 9 (2): 110. PubMed Abstract | Publisher Full Text | Free Full Text 56. Agranov M, Elliott M, Ortoleva P: The importance of Social Norms against Strategic Effects: The case of Covid-19 vaccine uptake. Econ. Lett. 2021; 206 : 109979. PubMed Abstract | Publisher Full Text | Free Full Text 57. Rosen B, Waitzberg R, Israeli A, et al. : Addressing vaccine hesitancy and access barriers to achieve persistent progress in Israel’s COVID-19 vaccination program. Isr. J. Health Policy Res. 2021; 10 (1): 20–43. PubMed Abstract | Publisher Full Text | Free Full Text 58. Gualano MR, Santoro PE, Borrelli I, et al. : Employee Participation in Workplace Vaccination Campaigns: A Systematic Review and Meta-Analysis. Vaccines. 2022; 10 (11). PubMed Abstract | Publisher Full Text | Free Full Text 59. Naseem A: Vaccine hesitancy dataset: figshare.2024 [cited 2024 Sep 11]. Reference Source 60. Naseem A: Vaccine hesitancy: machine learning model: figshare.2024 [cited 2024 Sep 11]. Reference Source 61. Naseem A, Rasool F, Carter WG: Supplemental files: figshare.2024. Reference Source 62. Naseem A: STROBE checklist: figshare.2024 [cited 2024 Sep 11]. Reference Source Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 17 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Punjab University College of Pharmacy, Faculty of Life Sciences, University of the Punjab, Lahore, Punjab, 05422, Pakistan 2 Allama Iqbal Medical College, Lahore, Punjab, 54700, Pakistan 3 Clinical Toxicology Research Group, School of Medicine, University of Nottingham Division of Medical Sciences and Graduate Entry Medicine, Derby, England, DE22 3DT, UK Ayesha Naseem Roles: Software, Writing – Original Draft Preparation Fatima Rasool Roles: Conceptualization, Investigation, Methodology, Supervision, Validation, Visualization, Writing – Review & Editing Furqan K. Haashmi Roles: Validation Muhammad Hamza Shoaib Roles: Writing – Original Draft Preparation Wayne G. Carter Roles: Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 17 Oct 2024, 13:1241 https://doi.org/10.12688/f1000research.155659.1 Copyright © 2024 Naseem A 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 Naseem A, Rasool F, Haashmi FK et al. Determinants of COVID-19 vaccine hesitancy in university students and support staff in Pakistan: A machine learning and statistical analysis [version 1; peer review: awaiting peer review] . F1000Research 2024, 13 :1241 ( https://doi.org/10.12688/f1000research.155659.1 ) 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: AWAITING PEER REVIEW AWAITING PEER REVIEW ? 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 Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 17 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status AWAITING PEER REVIEW Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects 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 = "Determinants of COVID-19 vaccine hesitancy...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-1241/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-1241/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-1241/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Naseem A 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/13-1241/v1/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/13-1241", templates : { twitter : "Determinants of COVID-19 vaccine hesitancy in university students.... Naseem A et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-1241/v1" } }; 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/155659/170856") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "170856"); $(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 = { "362503": 0, "362502": 0, "344973": 0, "344972": 0, "344975": 0, "344974": 0, "344969": 0, "362505": 0, "362504": 0, "344971": 0, "344970": 0, "362506": 0, "344977": 0, "344976": 0, "344978": 0, "353945": 0, "353944": 0, "376245": 0, "376244": 0, "376247": 0, "348854": 0, "376246": 0, "376243": 0, "336701": 0, "336700": 0, "376252": 0, "336703": 0, "336702": 0, "376249": 0, "376248": 0, "336699": 0, "376251": 0, "336698": 0, "376250": 0, "336705": 0, "336704": 0, "336707": 0, "336706": 0, "333013": 0, "333012": 0, "333015": 0, "333014": 0, "333021": 0, "333020": 0, "333017": 0, "333016": 0, "333019": 0, "333018": 0, "340085": 0, "340084": 0, "340087": 0, "340086": 0, "340081": 0, "340083": 0, "340082": 0, "357490": 0, "340089": 0, "340088": 0, "340090": 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 = "3f02ef5d-9bff-4c3f-934e-f118d9fbebf9"; 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(",","/")); } }); });