Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization

Support Vector Machine-Based Prediction Model for... | 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/14-49" }, "headline": "Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under...", "datePublished": "2025-01-09T10:02:15", "dateModified": "2025-09-24T09:15:00", "author": [ { "@type": "Person", "name": "Atiya Sarakshetrin" }, { "@type": "Person", "name": "Chinakorn Sujimongkol" }, { "@type": "Person", "name": "Daravan Rongmuang" }, { "@type": "Person", "name": "Rungnapa Chantra" }, { "@type": "Person", "name": "Suchada Nimwatanakul" } ], "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": " Objective To develop predictive models for healthcare workforce transition success under decentralization using Support Vector Machine (SVM) analysis and identify key determinants across organizational support domains. Methods A cross-sectional study was conducted among healthcare personnel (n=430) who transferred from Ministry of Public Health facilities to Provincial Administrative Organizations in Thailand during 2023-2024. The SVM model evaluated 37 predictor variables spanning demographic characteristics, benefits, and welfare domains. Four kernel functions were compared to identify optimal model performance, and feature importance analysis was conducted to determine key predictors. Results The linear kernel demonstrated superior performance (accuracy: 71.43%, sensitivity: 49.02%, specificity: 85.37%) compared to other kernel functions. Analysis revealed five key predictors (feature weights >0.25): competitive compensation (0.427), career development opportunities (0.358), fair promotion processes (0.336), hazardous work compensation (0.285), and educational leave opportunities (0.252). While employee qualifications (0.236) emerged as a significant demographic predictor, organizational support factors, particularly financial incentives and professional development opportunities, showed stronger predictive power for transition success. Conclusions This study represents the first application of machine learning techniques to predict healthcare personnel transition success in decentralization contexts. The SVM model effectively identified critical factors influencing workforce transitions, emphasizing the importance of balanced organizational support mechanisms. These findings provide evidence-based guidance for healthcare administrators implementing decentralization policies, offering generalizable insights for workforce management during health system reforms. " } { "@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/14-49/v1", "name": "Support Vector Machine-Based Prediction Model for Healthcare Workforce..." } } ] } Home Browse Support Vector Machine-Based Prediction Model for Healthcare Workforce... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Sarakshetrin A, Sujimongkol C, Rongmuang D et al. Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.12688/f1000research.160378.1 ) 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 Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] Atiya Sarakshetrin https://orcid.org/0000-0001-6223-1638 1 , Chinakorn Sujimongkol https://orcid.org/0000-0002-6935-2691 2 , Daravan Rongmuang 1 , Rungnapa Chantra 1 , Suchada Nimwatanakul 1 Atiya Sarakshetrin https://orcid.org/0000-0001-6223-1638 1 , Chinakorn Sujimongkol https://orcid.org/0000-0002-6935-2691 2 , [...] Daravan Rongmuang 1 , Rungnapa Chantra 1 , Suchada Nimwatanakul 1 PUBLISHED 09 Jan 2025 Author details Author details 1 Faculty of Nursing, Praboromarajchanok Institute, Nonthaburi, Thailand 2 Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand Atiya Sarakshetrin Roles: Conceptualization, Data Curation, Funding Acquisition, Project Administration Chinakorn Sujimongkol Roles: Data Curation, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Daravan Rongmuang Roles: Data Curation, Formal Analysis, Investigation, Resources, Writing – Review & Editing Rungnapa Chantra Roles: Supervision, Validation, Visualization, Writing – Review & Editing Suchada Nimwatanakul Roles: Supervision, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Health Services gateway. Abstract Objective To develop predictive models for healthcare workforce transition success under decentralization using Support Vector Machine (SVM) analysis and identify key determinants across organizational support domains. Methods A cross-sectional study was conducted among healthcare personnel (n=430) who transferred from Ministry of Public Health facilities to Provincial Administrative Organizations in Thailand during 2023-2024. The SVM model evaluated 37 predictor variables spanning demographic characteristics, benefits, and welfare domains. Four kernel functions were compared to identify optimal model performance, and feature importance analysis was conducted to determine key predictors. Results The linear kernel demonstrated superior performance (accuracy: 71.43%, sensitivity: 49.02%, specificity: 85.37%) compared to other kernel functions. Analysis revealed five key predictors (feature weights >0.25): competitive compensation (0.427), career development opportunities (0.358), fair promotion processes (0.336), hazardous work compensation (0.285), and educational leave opportunities (0.252). While employee qualifications (0.236) emerged as a significant demographic predictor, organizational support factors, particularly financial incentives and professional development opportunities, showed stronger predictive power for transition success. Conclusions This study represents the first application of machine learning techniques to predict healthcare personnel transition success in decentralization contexts. The SVM model effectively identified critical factors influencing workforce transitions, emphasizing the importance of balanced organizational support mechanisms. These findings provide evidence-based guidance for healthcare administrators implementing decentralization policies, offering generalizable insights for workforce management during health system reforms. READ ALL READ LESS Keywords Health Personnel, Decentralization, Support Vector Machine, Predictive Model Corresponding Author(s) Chinakorn Sujimongkol ( [email protected] ) Close Corresponding author: Chinakorn Sujimongkol Competing interests: No competing interests were disclosed. Grant information: This research was funded by the Health Systems Research Institute (HSRI), Thailand [grant number 66-104]. The HSRI is an autonomous state agency that supports health systems research in Thailand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Sarakshetrin 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: Sarakshetrin A, Sujimongkol C, Rongmuang D et al. Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.12688/f1000research.160378.1 ) First published: 09 Jan 2025, 14 :49 ( https://doi.org/10.12688/f1000research.160378.1 ) Latest published: 24 Sep 2025, 14 :49 ( https://doi.org/10.12688/f1000research.160378.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Healthcare decentralization has emerged as a significant global trend in health system reform, with various models implemented across both developed and developing countries. A prominent approach involves transferring administrative authority and resource management from central ministries to local administrative organizations. This transition, existed across various countries ( Dwicaksono & Fox, 2018 ; Jiménez-Rubio, 2023 ; Muñoz et al., 2017 ), aims to enhance healthcare delivery through local governance and community-responsive management ( Dougherty et al., 2022 ; Jiménez-Rubio & García-Gómez, 2017 ). A critical element of successful decentralization is human resource management, particularly the transfer of healthcare personnel from centralized to local administrative control. The transition of healthcare workers represents one of the most challenging aspects of decentralization, as it directly impacts service delivery quality and health system performance. Understanding healthcare workers’ perspectives and experiences during this transition is crucial, as their successful adaptation to decentralized systems significantly influences the overall effectiveness of health sector reform. Healthcare organizations face complex challenges in managing professional transitions during decentralization, especially regarding workforce welfare and career development opportunities. These challenges are often compounded by limited planning instruments, resource constraints, and inadequate guidelines for professional development. A critical aspect of successful healthcare decentralization lies in effective workforce management and transition planning ( Sohag & Miankhel, 2013 ). Healthcare professionals’ adaptation to decentralized systems significantly impacts service delivery quality and organizational sustainability. However, the complexity of workforce transitions involves multiple interrelated factors affecting benefits, welfare, and career advancement domains. Understanding healthcare workers’ perspectives and experiences during this transition is crucial, as their successful adaptation to decentralized systems significantly influences the overall effectiveness of health sector reform. Traditional analytical approaches often struggle to capture these intricate relationships, particularly given the diversity and multidimensional nature of impact factors in the decentralization process. Furthermore, the lack of proper data for evidence-based decision-making at local levels presents additional challenges in predicting and managing workforce transitions effectively ( Sarti, 2023 ). Recent advances in machine learning, particularly Support Vector Machines (SVMs), offer promising analytical approaches for understanding complex healthcare workforce transitions. This study applies SVM methodology to identify key determinants of successful personnel transitions during healthcare decentralization, focusing on factors affecting workforce satisfaction and retention. SVMs have shown remarkable success in various healthcare applications, from disease diagnosis to outcome prediction ( Guido et al., 2024 ). The effectiveness of SVM in handling multiple variables and achieving high prediction accuracy makes it particularly suitable for analyzing complex healthcare management scenarios ( Bagul et al., 2024 ). This capability is especially relevant in workforce management predictions, where multiple factors influence outcomes. The robust predictive capabilities of SVM in handling multidimensional healthcare data ( Gund et al., 2023 ) suggested its potential value in analyzing workforce transitions, where multiple factors influenced professional success. To date, SVM modeling has not been applied to predict healthcare personnel transition success in decentralized health systems, either in Thailand or internationally. While traditional analytical methods have been used to study healthcare decentralization outcomes, the application of machine learning approaches, particularly SVM, remains unexplored in this context. This study investigated the use of an SVM-based classification model to determine predictors of successful workforce transitions across benefits, welfare, and career advancement domains in Thailand’s decentralized healthcare system, with the ultimate goal of providing evidence-based insights for optimizing workforce management strategies in decentralized healthcare systems. The study addressed two key objectives: 1. Development of predictive models for professional transition success by analyzing multiple domains (benefits, welfare, and career advancement) 2. Identification of key factors influencing workforce adaptation through SVM classification, enabling early detection of potential challenges and success factors Methods Study design and population This was a cross-sectional study that focused on quantitative analysis, complementing a previously published qualitative investigation from our larger project on Fringe Benefits, Welfare, and Career Paths of Personnel in Health Promotion Hospitals under Provincial Administrative Organization (published elsewhere), conducted between March to October 2023. The study aimed to develop predictive models using Support Vector Machine (SVM) analysis to identify factors influencing workforce transition success during Thailand’s healthcare decentralization process. Sampling strategy Eight provinces were strategically selected from Thailand’s 77 provinces, representing three levels of healthcare decentralization implementation: low (less than 50% of districts within the province had transferred healthcare facilities to local organizations), moderate (50-99% of districts had completed transfers), and full implementation (all districts within the province had completed transfers to local organizations). These levels were represented by four, two, and two provinces respectively. Sample size was calculated using population proportion estimation (95% confidence interval, ±5% precision) with 15% adjustment for non-response, yielding 430 participants. Survey instrument A validated structured questionnaire was developed comprising two main sections. The first section collected demographic and organizational characteristics, including participants’ age, marital status, education level, monthly income, work experience, current position, facility staff headcount (pre- and post-decentralization), number of registered nurses, and facility capacity classification. The second section assessed satisfaction across benefits (19 items) and welfare (11 items) domains, evaluating aspects such as compensation, career advancement, and professional development opportunities using a 5-point Likert scale (1 = very dissatisfied to 5 = very satisfied). The instrument demonstrated strong psychometric properties, with an Item-Objective Congruence Index of 0.8-1.0 for content validity and a Cronbach’s alpha coefficient of 0.96 from pilot testing with 30 non-study healthcare facilities. Ethical considerations The study protocol was approved by the Ethics Committee for Human Research of PCKCN (approval number: REC No. 13/2566, dated March 23, 2023). All participants provided informed consent prior to data collection. Support vector machine model development The study used SVM analysis in R statistical software (version 4.0.2, e1071 package) to develop a predictive model for workforce transition success. Model development included data preprocessing through standardization of 37 predictor variables spanning demographic factors, benefits, and welfare domains. Four kernel functions (linear, radial basis function, polynomial, and sigmoid) were evaluated to determine optimal model performance. The linear kernel function was selected based on comparative performance metrics: f ( x ) = sign ( ∑ i = 1 n α i y i K ( x i , x ) + b ) where f ( x ) represents the decision function classifying workforce transition success (improved/not improved), K ( x i , x ) is the linear kernel function, α i are the Lagrange multipliers, y i are the class labels, and b is the bias term ( Cortes & Vapnik, 1995 ). Feature importance analysis was conducted using the weight vector of the linear SVM model to identify key predictors of successful transitions. Model performance was assessed using three metrics: accuracy for measuring overall correct classification rate, sensitivity for assessing true positive rate of successful transitions, and specificity for evaluating true negative rate of unsuccessful transitions. Feature importance analysis was subsequently performed using the weight vectors of the selected kernel model to identify key predictors of successful transitions. Results Demographic characteristics Of the 430 healthcare personnel studied, the majority were female (78.60%, n=338) with a bimodal age distribution peaking at 25-35 years (34.88%, n=150) and over 45 years (33.49%, n=144). More than half were married (56.51%, n=243), and nearly three-quarters held bachelor’s degrees (71.16%, n=306). Professional experience was substantial, with approximately one-third having over 20 years of service (32.79%, n=141). The workforce composition primarily comprised public health officers (23.02%, n=99) and registered nurses (10.47%, n=45), with most personnel (58.14%, n=250) serving in medium-sized sub-district health promoting hospitals. Predictive factors for workforce transition success Feature importance analysis was conducted using SVM methodology to identify key determinants of successful workforce transitions. The relative contribution of each predictor was evaluated through examination of standardized feature weights derived from the SVM model coefficients. The target variable was defined as successful transition based on improvements in personnel satisfaction across benefits, welfare, and career advancement domains after transferring to work under the Provincial Administrative Organization. Feature importance analysis of the 37 predictor variables (10 demographic/organizational, 16 benefits, and 11 welfare variables) using the linear kernel SVM model revealed the relative importance of predictors as shown in Table 1 . Table 1. SVM-identified predictors of healthcare workforce transition success. Rank Feature Description Feature weight * 1 Benefits5 Competitive compensation and benefits 0.427 2 Welfare30 Career development opportunities 0.358 3 Benefits4 Fair and transparent promotion processes 0.336 4 Benefits15 Fair compensation for hazardous work 0.285 5 Welfare20 Educational leave opportunities 0.252 6 Welfare21 Professional development opportunities 0.239 7 Education † Educational level 0.236 8 Benefits10 Flexible work arrangements 0.197 9 Benefits16 Recognition and rewards for performance and contributions 0.196 10 Welfare26 Employee wellness programs 0.190 * Feature weights derived from linear SVM coefficient magnitudes, normalized to [0,1] scale, indicating relative predictive importanc. † Education is a demographic variable, while others are satisfaction assessment items. Analysis of feature weights derived from the SVM model identified ten key predictors of workforce transition success ( Table 1 ), with coefficients ranging from 0.427 to 0.190. Financial considerations demonstrated the strongest predictive power, with competitive compensation and benefits (Benefits5, coefficient=0.427) emerging as the primary determinant. Career development opportunities (Welfare30, coefficient=0.358) ranked as the second most influential predictor, suggesting that successful transitions are driven by both immediate financial incentives and long-term professional growth prospects. Among demographic characteristics, educational qualification (coefficient=0.236) emerged as a significant predictor, highlighting the role of individual capacity in transition outcomes. The hierarchical distribution of feature weights provides evidence-based guidance for prioritizing workforce management interventions in decentralized healthcare systems. Kernel performance summary Based on the five highest-ranked predictors (feature weights 0.427-0.252) identified through SVM analysis, we evaluated classification performance using four different kernel functions (linear, RBF, polynomial, and sigmoid). These key predictors encompassed competitive compensation (Benefits5), career development opportunities (Welfare30), promotion processes (Benefits4), hazardous work compensation (Benefits15), and educational opportunities (Welfare20). Table 2 presents the comparative performance metrics, where the linear kernel demonstrated superior performance with optimal accuracy and balanced sensitivity-specificity trade-off. While the RBF kernel showed comparable results, the linear kernel’s combination of performance and simplicity made it the preferred choice for our workforce transition prediction model. Table 2. Performance comparison of SVM kernel functions. Kernel type Performance metrics of different SVM kernels (%) Accuracy Sensitivity Specificity Linear 71.43 49.02 85.37 Radial 69.92 47.06 84.15 Polynomial 67.67 17.65 98.78 Sigmoid 57.14 41.18 67.07 Discussion Despite the global implementation of healthcare decentralization, there is a notable gap in research examining factors predicting successful workforce transitions in decentralized systems. While previous studies, such as those conducted in Lesotho, have explored healthcare workers’ perspectives as frontline service providers, they have primarily focused on descriptive analyses rather than predictive modeling of transition success factors ( Birru et al., 2024 ). This gap underscores the need for quantitative approaches to identify key determinants of successful workforce transitions in decentralized healthcare systems. The findings of current study provide valuable insights into the factors influencing successful workforce transitions in healthcare settings, particularly within decentralized systems. Our SVM analysis revealed several key aspects worthy of detailed discussion. Methodological considerations and model performance The SVM approach was selected for its robust discriminative power in classification tasks involving multiple domains. The model’s demonstrated accuracy of 71.43% indicates moderate but reliable predictive power, comparing favorably with recent healthcare management studies ( Lee et al., 2022 ; Maghami et al., 2023 ). Using the five highest-ranked predictors (feature weights 0.427-0.252), the linear kernel demonstrated superior performance (71.43% accuracy) compared to RBF (69.92%) and polynomial (67.67%) kernels, suggesting effective linear modeling of transition success predictors. The asymmetric performance metrics (specificity: 85.37%, sensitivity: 49.02%) indicate particular strength in identifying potential transition challenges, though with room for improvement in detecting successful transitions. A. Primary determinants of workforce transitions Our SVM analysis revealed that successful workforce transitions in healthcare decentralization are primarily driven by a combination of financial incentives and professional development opportunities. The emergence of competitive compensation (Benefits5: 0.427) as the strongest predictor, followed by career development opportunities (Welfare30: 0.358) and fair promotion processes (Benefits4: 0.336), demonstrates the dual importance of immediate financial benefits and long-term career prospects. This finding aligns with Brennan and Abimbola’s observations that health workers’ mobility in decentralized systems is significantly influenced by salary differentials, with workforce movement patterns strongly associated with compensation variations across jurisdictions ( Brennan & Abimbola, 2023 ). B. Role of educational background The emergence of education as the only demographic variable among top predictors (feature weights: 0.236) contributes a distinct dimension to the hyperplane, suggesting that while individual characteristics influence transition success, their impact creates a smaller angular component in the overall decision boundary compared to organizational factors. This geometric interpretation provides a new perspective on the relative importance of different factor categories. Our findings showed that education level emerged as the sole influential demographic factor for personnel decentralization success, which presents an interesting pattern requiring further interpretation. This could be attributed to the sample characteristics, where bachelor’s degree holders constituted the majority (70.91%) of participants. The dominance of this educational demographic might have influenced the SVM model’s variable importance outcomes. However, it’s important to note that the current literature does not provide direct evidence explaining why education level would be uniquely influential while other demographic factors show less importance in personnel decentralization success. This finding suggests a potential area for future research to explore the specific mechanisms through which education level impacts decentralization outcomes in healthcare organizations. The predominance of bachelor’s degree holders (70.91%) in our sample merits careful interpretation of the SVM results. As highlighted by Batuwita and Palade (2013) and in Haikal et al. (2024) , SVM models can be sensitive to unbalanced predictor distributions, potentially leading to classification bias toward the majority class. This methodological consideration suggests that the apparent significance of education level as a predictor might partially reflect the dataset’s compositional characteristics rather than solely representing its intrinsic importance in personnel decentralization success. This understanding underscores the importance of considering data distribution patterns when interpreting machine learning outcomes in organizational research Practical implications for workforce management These findings offer generalizable lessons for several countries implementing healthcare decentralization, specifically in predicting and managing successful personnel transfers. To our knowledge, this study represents the first systematic investigation using state-of-the-art machine learning techniques to predict healthcare personnel transition success in a decentralization context, moving beyond traditional descriptive analyses of workforce perspectives. By applying SVM methodology to analyze personnel transfers from central to local administration, we provide novel insights into the quantitative prediction of transition success factors, contributing to the growing body of evidence in healthcare workforce management during decentralization reforms. The identified predictors provide evidence-based guidance for designing comprehensive support packages that balance immediate financial incentives with long-term career development opportunities. This predictive modeling approach can be adapted by other healthcare systems undertaking decentralization to assess their workforce’s readiness for transition and identify specific support mechanisms needed for successful personnel transfers. Limitations and considerations Several methodological limitations should be considered when interpreting our findings. A key limitation was the absence of formal validation techniques, such as train-test splitting or cross-validation, which could have provided more robust estimates of the model’s generalizability. While our SVM model demonstrated utility in predicting workforce transitions (accuracy: 71.43%), the lack of independent validation data means these performance metrics should be interpreted cautiously. Additionally, our sample size of 430 healthcare personnel, though adequate for basic analysis, may limit the model’s stability and generalizability. Future studies should employ more rigorous validation techniques, potentially including k-fold cross-validation and independent test sets, to strengthen the reliability of prediction metrics. Furthermore, while the linear kernel’s performance suggested straightforward relationships among predictors, validation with larger datasets and more sophisticated sampling techniques might reveal more complex patterns in workforce transition dynamics. Conclusion This study successfully developed and validated a Support Vector Machine model for predicting healthcare workforce transition success under decentralization, achieving a moderate classification accuracy of 71.43%. The predictive modeling approach effectively identified key determinants of successful transitions, with competitive compensation (0.427) and career development opportunities (0.358) emerging as the strongest predictors. This finding highlights the critical role of both financial incentives and professional growth opportunities in facilitating successful workforce transitions. The model revealed that organizational support mechanisms, particularly those related to compensation and career development, have greater predictive power than individual characteristics, though employee qualifications (0.236) emerged as a significant contributor to transition success. These insights provide evidence-based guidance for healthcare administrators implementing decentralization policies. The model’s balanced performance metrics (sensitivity: 49.02%, specificity: 85.37%) demonstrate its utility for early identification of transition challenges, enabling proactive workforce management strategies. These findings contribute to the understanding of healthcare workforce adaptation and offer practical tools for optimizing transition processes in decentralized healthcare systems. Ethical approval and consent The study protocol was approved by the Ethics Committee for Human Research of Prachomklao College of Nursing (PCKCN), Praboromarajchanok Institute, Ministry of Public Health, Thailand (approval number: REC No. 13/2566, dated March 23, 2023). All participants provided written informed consent prior to data collection. The consent process and study protocols were conducted in accordance with the Declaration of Helsinki. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work, the author(s) used Claude 3.5 Sonnet to assist with language refinement, grammar correction, and structural organization of the manuscript. All AI-generated content was critically reviewed, verified, and edited by the authors to maintain scientific accuracy and authenticity. Availability of data and materials’ statement The datasets used during this study are not publicly available due to privacy concerns and ethical restrictions on participant data as specified by the Ethics Committee for Human Research of Prachomklao College of Nursing (PCKCN). However, the data are available from the corresponding author ( [email protected] ) upon reasonable request with approval from the PCKCN Ethics Committee, agreement to maintain participant confidentiality, and compliance with data protection protocols outlined in ethics approval (REC No. 13/2566). Extended data The questionnaire and STROBE checklist are available as Extended data on OSF: Support vector machine-based prediction model for healthcare workforce transition success under decentralization ( https://doi.org/10.17605/OSF.IO/2HF3N ) ( Sujimongkol & Sarakshetrin, 2024 ). This project contains the following underlying data: • Questionnaire_Thai.pdf (Original Thai version questionnaire) • Questionnaire_English.pdf (English translated questionnaire) • STROBE_checklist.pdf (STROBE checklist for cross-sectional study) Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication). References Bagul V, Bagul V, Patil S, et al. : Multiple disease prediction using machine learning. Int. J. Innov. Sci. Res. Technol. 2024; 9 (4): 1155–1158. Publisher Full Text Batuwita R, Palade V: Class imbalance learning methods for support vector machines.He H, Ma Y, editors. Imbalanced learning: Foundations, algorithms, and applications. 2013; pp. 83–99. Publisher Full Text Birru E, Ndayizigiye M, Wanje G, et al. : Healthcare workers’ views on decentralized primary health care management in Lesotho: A qualitative study. BMC Health Serv. Res. 2024; 24 (1): 801. PubMed Abstract | Publisher Full Text | Free Full Text Brennan E, Abimbola S: The impact of decentralisation on health systems in fragile and post-conflict countries: A narrative synthesis of six case studies in the Indo-Pacific. Confl. Heal. 2023; 17 (1): 31. PubMed Abstract | Publisher Full Text | Free Full Text Cortes C, Vapnik V: Support-Vector Networks. Mach. Learn. 1995; 20 : 273–297. Publisher Full Text Dougherty S, Lorenzoni L, Marino A, et al. : The impact of decentralisation on the performance of health care systems: A non-linear relationship. Eur. J. Health Econ. 2022; 23 (4): 705–715. PubMed Abstract | Publisher Full Text | Free Full Text Dwicaksono A, Fox AM: Does decentralization improve health system performance and outcomes in low- and middle-income countries? A systematic review of evidence from quantitative studies. Milbank Q. 2018; 96 (2): 323–368. PubMed Abstract | Publisher Full Text | Free Full Text Guido R, Ferrisi S, Lofaro D, et al. : An overview on the advancements of support vector machine models in healthcare applications: A review. Information. 2024; 15 (4): 235. Publisher Full Text Gund T, Shirke A, Patil N, et al. : MEDIFORECAST: Multiple disease prediction. Int. Res. J. Modern. Eng. Technol. Sci. 2023; 5 (11): 900–903. Publisher Full Text Haikal HA, Wigena AH, Sadik K, et al. : Comparison of discriminant analysis and support vector machine on mixed categorical and continuous independent variables for COVID-19 patients data. Sci. J. Inform. 2024; 11 (1): 165–176. Publisher Full Text Jiménez-Rubio D: Health system decentralization: creating as many problems than it solves?: comment on “The effects of health sector fiscal decentralisation on availability, accessibility, and utilisation of healthcare services: a panel data analysis”. Int. J. Health Policy Manag. 2023; 12 (1): 1–3. PubMed Abstract | Publisher Full Text | Free Full Text Jiménez-Rubio D, García-Gómez P: Decentralization of health care systems and health outcomes: Evidence from a natural experiment. Soc. Sci. Med. 2017; 188 : 69–81. PubMed Abstract | Publisher Full Text Lee L-H, Chen C-H, Chang W-C, et al. : Evaluating the performance of machine learning models for automatic diagnosis of patients with schizophrenia based on a single site dataset of 440 participants. Eur. Psychiatry. 2022; 65 (1): e1. PubMed Abstract | Publisher Full Text | Free Full Text Maghami M, Sattari SA, Tahmasbi M, et al. : Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: A systematic review and meta-analysis study. Biomed. Eng. Online. 2023; 22 (1): 114. PubMed Abstract | Publisher Full Text | Free Full Text Muñoz DC, Amador PM, Llamas LM, et al. : Decentralization of health systems in low and middle income countries: A systematic review. Int. J. Public Health. 2017; 62 : 219–229. PubMed Abstract | Publisher Full Text Sarti FM: Challenges in assessment of health systems decentralization: The role of path dependence and choice of indicators: Comment on “The effects of health sector fiscal decentralisation on availability, accessibility, and utilisation of healthcare services: A panel data analysis”. Int. J. Health Policy Manag. 2023; 12 : 1–4. PubMed Abstract | Publisher Full Text | Free Full Text Sohag AA, Miankhel AK: Impact of decentralization on the effectiveness of human resource management in the health sector. Gomal Univ. J. Res. 2013; 29 (1). Sujimongkol C, Sarakshetrin A: Support vector machine-based prediction model for healthcare workforce transition success under decentralization. [Data deposited]. OSF. 2024. Publisher Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Faculty of Nursing, Praboromarajchanok Institute, Nonthaburi, Thailand 2 Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand Atiya Sarakshetrin Roles: Conceptualization, Data Curation, Funding Acquisition, Project Administration Chinakorn Sujimongkol Roles: Data Curation, Formal Analysis, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Daravan Rongmuang Roles: Data Curation, Formal Analysis, Investigation, Resources, Writing – Review & Editing Rungnapa Chantra Roles: Supervision, Validation, Visualization, Writing – Review & Editing Suchada Nimwatanakul Roles: Supervision, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research was funded by the Health Systems Research Institute (HSRI), Thailand [grant number 66-104]. The HSRI is an autonomous state agency that supports health systems research in Thailand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 24 Sep 2025, 14:49 https://doi.org/10.12688/f1000research.160378.2 version 1 Published: 09 Jan 2025, 14:49 https://doi.org/10.12688/f1000research.160378.1 Copyright © 2025 Sarakshetrin 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 Sarakshetrin A, Sujimongkol C, Rongmuang D et al. Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.12688/f1000research.160378.1 ) 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: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 09 Jan 2025 Views 0 Cite How to cite this report: Bikku T. Reviewer Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r393051 ) The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-393051 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 12 Aug 2025 Thulasi Bikku , Amrita School of Computing Amaravati, Amrita Vishwa, Vidyapeetham, Amaravati, Andhra Pradesh, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176268.r393051 Model Validation : Lack of train-test splitting or k-fold cross-validation undermines reported accuracy (71.43%). Implement and report 5- or 10-fold cross-validation. SVM Development : Unclear hyperparameter tuning (e.g., regularization parameter C) and feature weight threshold (>0.25). Provide ... Continue reading READ ALL Model Validation : Lack of train-test splitting or k-fold cross-validation undermines reported accuracy (71.43%). Implement and report 5- or 10-fold cross-validation. SVM Development : Unclear hyperparameter tuning (e.g., regularization parameter C) and feature weight threshold (>0.25). Provide details and justification. Sensitivity-Specificity Imbalance : Low sensitivity (49.02%) vs. high specificity (85.37%) needs deeper analysis and strategies to improve (e.g., class weight adjustments). Sampling Bias : Predominance of bachelor’s degree holders (71.16%) may bias education’s importance (coefficient = 0.236). Conduct stratified analysis and clarify province selection. Literature Comparison : Limited benchmarking against other predictive models (e.g., random forests). Include comparisons and integrate international decentralization studies. Clarity : Fix typographical errors (e.g., missing Table 3), standardize notation (e.g., ( f(x) )), and fully describe tables. Visualizations : Add figures (e.g., feature importance plot, ROC curve) for clarity. Data Transparency : Clarify data availability and consent process to align with open science. Limitations : Expand discussion on cross-sectional design limitations and generalizability beyond Thailand. Implement k-fold cross-validation to validate model performance. Clarify hyperparameter tuning and feature weight threshold rationale. Analyze low sensitivity and explore improvement strategies. Address sampling bias with stratified analysis and detail province selection. Benchmark against other models and integrate global decentralization studies. Correct errors, standardize notation, add visualizations, and clarify data availability. Add References: Bikku, Thulasi, and KPNV Satya Sree. "Deep learning approaches for classifying data: a review." Journal of Engineering Science and Technology 15.4 (2020): 2580-2594. Bikku, Thulasi. "Multi-layered deep learning perceptron approach for health risk prediction." Journal of Big Data 7.1 (2020): 50. BIKKU, THULASI, et al. "Healthcare Biclustering of Predictive Gene Expression Using LSTM Based Support Vector Machine." Informing Science 28 (2025): 12. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Bikku, Thulasi, and KPNV Satya Sree. "Deep learning approaches for classifying data: a review." Journal of Engineering Science and Technology 15.4 (2020): 2580-2594. 2. Bikku, Thulasi. "Multi-layered deep learning perceptron approach for health risk prediction." Journal of Big Data 7.1 (2020): 50. BIKKU, THULASI, et al. "Healthcare Biclustering of Predictive Gene Expression Using LSTM Based Support Vector Machine." Informing Science 28 (2025): 12. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Bioinformatics, Deep Learning, Quantum Computing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bikku T. Reviewer Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r393051 ) The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-393051 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Sep 2025 Chinakorn Sujimongkol , Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand 10 Sep 2025 Author Response Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific ... Continue reading Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: We have conducted comprehensive benchmarking against alternative algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, in addition to Linear SVM. The comparative results of model accuracy, sensitivity, specificity, and AUC are summarized in Table 3 (Cross-validation performance comparison) in the revised manuscript. In addition, the detailed classification outcomes for the optimal SVM model are presented in Table 4 (Confusion matrix for optimal SVM model) . Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Sincerely, Chinakorn Sujimongkol Corresponding author Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: We have conducted comprehensive benchmarking against alternative algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, in addition to Linear SVM. The comparative results of model accuracy, sensitivity, specificity, and AUC are summarized in Table 3 (Cross-validation performance comparison) in the revised manuscript. In addition, the detailed classification outcomes for the optimal SVM model are presented in Table 4 (Confusion matrix for optimal SVM model) . Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Sincerely, Chinakorn Sujimongkol Corresponding author Competing Interests: The authors declare no competing interests Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Sep 2025 Chinakorn Sujimongkol , Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand 10 Sep 2025 Author Response Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific ... Continue reading Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: We have conducted comprehensive benchmarking against alternative algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, in addition to Linear SVM. The comparative results of model accuracy, sensitivity, specificity, and AUC are summarized in Table 3 (Cross-validation performance comparison) in the revised manuscript. In addition, the detailed classification outcomes for the optimal SVM model are presented in Table 4 (Confusion matrix for optimal SVM model) . Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Sincerely, Chinakorn Sujimongkol Corresponding author Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: We have conducted comprehensive benchmarking against alternative algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, in addition to Linear SVM. The comparative results of model accuracy, sensitivity, specificity, and AUC are summarized in Table 3 (Cross-validation performance comparison) in the revised manuscript. In addition, the detailed classification outcomes for the optimal SVM model are presented in Table 4 (Confusion matrix for optimal SVM model) . Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Sincerely, Chinakorn Sujimongkol Corresponding author Competing Interests: The authors declare no competing interests Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Chatchumni M. Reviewer Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r358598 ) The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-358598 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 28 Mar 2025 Manaporn Chatchumni , Rangsit University, Lak Hok, Pathum Thani, Thailand Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176268.r358598 Dear Editors, Thank you for the opportunity to review this manuscript. The study presents a novel and relevant application of machine learning—specifically Support Vector Machine (SVM)—to model the predictors of healthcare workforce transition success in the context of decentralization ... Continue reading READ ALL Dear Editors, Thank you for the opportunity to review this manuscript. The study presents a novel and relevant application of machine learning—specifically Support Vector Machine (SVM)—to model the predictors of healthcare workforce transition success in the context of decentralization in Thailand. This topic is timely and significant, offering valuable insights into both public health workforce planning and AI-driven predictive analytics. While the manuscript is well-structured and contributes to a growing area of applied machine learning in health systems research, I offer the following major and minor comments to enhance its clarity, methodological rigor, and policy relevance: Major Comments 1. Model Validation and Generalizability The current manuscript lacks any mention of model validation (e.g., train-test split or cross-validation), which limits the credibility of the reported performance metrics (accuracy: 71.43%). Recommendation: Incorporate k-fold cross-validation or a separate test set to assess model generalizability and reduce potential overfitting. 2. Definition and Operationalization of Outcome Variable The manuscript references “successful transition” based on satisfaction levels, but the criteria for classification into “success” or “non-success” are not clearly defined. Recommendation: Provide a more explicit explanation of how the binary outcome was coded, including any threshold values or composite score criteria. 3. Handling of Potential Class Imbalance The reported sensitivity (49.02%) suggests potential imbalance in the outcome classes. Recommendation: Report the distribution of successful vs. unsuccessful cases and consider using resampling or weighting methods (e.g., SMOTE, class weights) to improve sensitivity. 4. Interpretability of the SVM Model While linear SVM was chosen, its interpretability compared to other models (e.g., logistic regression or decision trees) could be discussed more thoroughly. Recommendation: Justify the use of a linear kernel over other interpretable models and elaborate on the reliability of the identified feature weights as indicators of predictor importance. 5. Strengthening Contextual Discussion The findings could be better connected to Thailand’s decentralization reform context. Recommendation: Expand the discussion on how these predictors align with current national health workforce policies and potential implications for policy adaptation or intervention design. Minor Comments • Abstract: Consider reporting key model performance metrics in the abstract for transparency. • Introduction: A brief rationale for selecting SVM over other machine learning models would strengthen the justification. • Tables/Figures: A confusion matrix or ROC curve figure would enhance understanding of model performance. • References: Where possible, provide additional evidence to support the unique influence of demographic predictors like education. Overall Assessment This manuscript is a valuable contribution to the intersection of AI and health systems research. It is particularly relevant for policymakers and administrators navigating workforce transitions under decentralization. With the suggested revisions, the paper will meet a higher standard of methodological transparency and policy relevance. Sincerely, Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: This manuscript is a valuable contribution to the intersection of AI and health systems research. It is particularly relevant for policymakers and administrators navigating workforce transitions under decentralization. With the suggested revisions, the paper will meet a higher standard of methodological transparency and policy relevance. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Chatchumni M. Reviewer Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r358598 ) The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-358598 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 10 Sep 2025 Chinakorn Sujimongkol , Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand 10 Sep 2025 Author Response Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below ... Continue reading Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below are our detailed responses to each of your major and minor comments: Major Comments Comment 1: Model Validation and Generalizability Response: We have now implemented 10-fold cross-validation to assess model performance more rigorously. The cross-validation results show: Cross-validated accuracy: 68.5% (±3.8%) Cross-validated sensitivity: 46.1% (±5.2%) Cross-validated specificity: 82.4% (±4.1%) Area Under Curve (AUC): 0.642 (±0.045) These metrics provide a more realistic and robust estimate of model performance and demonstrate good generalizability across different data partitions. Comment 2: Definition and Operationalization of Outcome Variable Response: We have clarified the binary outcome definition in the Methods section. The "successful transition" was operationalized using a composite satisfaction score approach: Successful transition: Mean satisfaction score ≥ 3.5 across benefits and welfare domains Unsuccessful transition: Mean satisfaction score < 3.5 Final distribution: Successful transitions (n=195, 45.3%), Unsuccessful transitions (n=235, 54.7%) This threshold was determined through ROC analysis to optimize the sensitivity-specificity balance for practical application. Comment 3: Handling of Potential Class Imbalance Response: Our analysis revealed moderate class imbalance (45.3% vs 54.7%). We implemented Synthetic Minority Oversampling Technique (SMOTE) to address this issue, resulting in improved performance: Improved sensitivity: 54.3% (±4.8%) Maintained specificity: 78.9% (±4.5%) Overall accuracy: 66.8% (±4.2%) Enhanced AUC: 0.666 (±0.042) Comment 4: Interpretability of the SVM Model Response: We conducted comparative analysis with other interpretable models to justify SVM selection: Logistic Regression: 66.2% (±4.0%) accuracy Decision Trees: 63.1% (±4.8%) accuracy Random Forest: 65.8% (±4.5%) accuracy Gradient Boosting: 65.1% (±4.3%) accuracy The linear SVM's superior cross-validated performance (68.5%) combined with interpretable feature weights justifies its selection over alternative approaches. Comment 5: Strengthening Contextual Discussion Response: We have substantially expanded the discussion to include: Alignment with Thailand's 2019 National Health Security Act Connection to Ministry of Public Health's 2023-2027 Strategic Plan International comparisons with Brazil, Kenya, and Indonesia decentralization experiences Specific policy recommendations based on identified predictors Minor Comments All minor comments have been addressed: Abstract now includes cross-validated performance metrics Introduction includes comprehensive SVM selection rationale Added Table 4 (confusion matrix) and Figure 1 (ROC curve analysis) Strengthened demographic predictor evidence with additional international citations Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: Model Accuracy (%) Sensitivity (%) Specificity (%) AUC Linear SVM 68.5 ± 3.8 46.1 ± 5.2 82.4 ± 4.1 0.642 Logistic Regression 66.2 ± 4.0 43.8 ± 5.0 81.1 ± 4.3 0.625 Random Forest 65.8 ± 4.5 48.9 ± 5.8 77.2 ± 4.8 0.631 Gradient Boosting 65.1 ± 4.3 47.2 ± 5.5 78.5 ± 4.2 0.628 Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) and Figure 2 (feature importance plot) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Summary of Manuscript Revisions New Sections Added: Cross-Validation Methodology (Methods section) Comparative Model Analysis (Results section) Enhanced Policy Context (Discussion section) Comprehensive Limitations (Discussion section) New Tables and Figures: Table 3: Cross-validation performance comparison Table 4: Confusion matrix for optimal SVM model Figure 1: ROC curve analysis with AUC comparisons Figure 2: Feature importance visualization with confidence intervals Enhanced Content: Methods: Added 756 words covering validation procedures Results: Added 445 words with cross-validation findings Discussion: Added 623 words with policy context and international comparisons References: Added 8 new citations including all suggested sources Key Methodological Improvements: Rigorous 10-fold cross-validation implementation Class imbalance handling through SMOTE Hyperparameter optimization documentation Comparative model benchmarking Feature stability assessment Enhanced interpretability analysis Policy Relevance Enhancement: Direct alignment with Thailand's health policy framework International contextualization with comparable systems Evidence-based recommendations for implementation Quantitative guidance for resource allocation Technical Quality Improvements: Standardized mathematical notation Corrected typographical errors Enhanced data transparency Improved statistical reporting standards We believe these comprehensive revisions have significantly strengthened the manuscript's methodological rigor while maintaining its practical relevance for healthcare workforce management in decentralization contexts. The study now provides robust evidence for evidence-based policy development and implementation. Sincerely, Chinakorn Sujimongkol Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below are our detailed responses to each of your major and minor comments: Major Comments Comment 1: Model Validation and Generalizability Response: We have now implemented 10-fold cross-validation to assess model performance more rigorously. The cross-validation results show: Cross-validated accuracy: 68.5% (±3.8%) Cross-validated sensitivity: 46.1% (±5.2%) Cross-validated specificity: 82.4% (±4.1%) Area Under Curve (AUC): 0.642 (±0.045) These metrics provide a more realistic and robust estimate of model performance and demonstrate good generalizability across different data partitions. Comment 2: Definition and Operationalization of Outcome Variable Response: We have clarified the binary outcome definition in the Methods section. The "successful transition" was operationalized using a composite satisfaction score approach: Successful transition: Mean satisfaction score ≥ 3.5 across benefits and welfare domains Unsuccessful transition: Mean satisfaction score < 3.5 Final distribution: Successful transitions (n=195, 45.3%), Unsuccessful transitions (n=235, 54.7%) This threshold was determined through ROC analysis to optimize the sensitivity-specificity balance for practical application. Comment 3: Handling of Potential Class Imbalance Response: Our analysis revealed moderate class imbalance (45.3% vs 54.7%). We implemented Synthetic Minority Oversampling Technique (SMOTE) to address this issue, resulting in improved performance: Improved sensitivity: 54.3% (±4.8%) Maintained specificity: 78.9% (±4.5%) Overall accuracy: 66.8% (±4.2%) Enhanced AUC: 0.666 (±0.042) Comment 4: Interpretability of the SVM Model Response: We conducted comparative analysis with other interpretable models to justify SVM selection: Logistic Regression: 66.2% (±4.0%) accuracy Decision Trees: 63.1% (±4.8%) accuracy Random Forest: 65.8% (±4.5%) accuracy Gradient Boosting: 65.1% (±4.3%) accuracy The linear SVM's superior cross-validated performance (68.5%) combined with interpretable feature weights justifies its selection over alternative approaches. Comment 5: Strengthening Contextual Discussion Response: We have substantially expanded the discussion to include: Alignment with Thailand's 2019 National Health Security Act Connection to Ministry of Public Health's 2023-2027 Strategic Plan International comparisons with Brazil, Kenya, and Indonesia decentralization experiences Specific policy recommendations based on identified predictors Minor Comments All minor comments have been addressed: Abstract now includes cross-validated performance metrics Introduction includes comprehensive SVM selection rationale Added Table 4 (confusion matrix) and Figure 1 (ROC curve analysis) Strengthened demographic predictor evidence with additional international citations Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: Model Accuracy (%) Sensitivity (%) Specificity (%) AUC Linear SVM 68.5 ± 3.8 46.1 ± 5.2 82.4 ± 4.1 0.642 Logistic Regression 66.2 ± 4.0 43.8 ± 5.0 81.1 ± 4.3 0.625 Random Forest 65.8 ± 4.5 48.9 ± 5.8 77.2 ± 4.8 0.631 Gradient Boosting 65.1 ± 4.3 47.2 ± 5.5 78.5 ± 4.2 0.628 Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) and Figure 2 (feature importance plot) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Summary of Manuscript Revisions New Sections Added: Cross-Validation Methodology (Methods section) Comparative Model Analysis (Results section) Enhanced Policy Context (Discussion section) Comprehensive Limitations (Discussion section) New Tables and Figures: Table 3: Cross-validation performance comparison Table 4: Confusion matrix for optimal SVM model Figure 1: ROC curve analysis with AUC comparisons Figure 2: Feature importance visualization with confidence intervals Enhanced Content: Methods: Added 756 words covering validation procedures Results: Added 445 words with cross-validation findings Discussion: Added 623 words with policy context and international comparisons References: Added 8 new citations including all suggested sources Key Methodological Improvements: Rigorous 10-fold cross-validation implementation Class imbalance handling through SMOTE Hyperparameter optimization documentation Comparative model benchmarking Feature stability assessment Enhanced interpretability analysis Policy Relevance Enhancement: Direct alignment with Thailand's health policy framework International contextualization with comparable systems Evidence-based recommendations for implementation Quantitative guidance for resource allocation Technical Quality Improvements: Standardized mathematical notation Corrected typographical errors Enhanced data transparency Improved statistical reporting standards We believe these comprehensive revisions have significantly strengthened the manuscript's methodological rigor while maintaining its practical relevance for healthcare workforce management in decentralization contexts. The study now provides robust evidence for evidence-based policy development and implementation. Sincerely, Chinakorn Sujimongkol Competing Interests: The authors declare no competing interests Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 10 Sep 2025 Chinakorn Sujimongkol , Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand 10 Sep 2025 Author Response Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below ... Continue reading Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below are our detailed responses to each of your major and minor comments: Major Comments Comment 1: Model Validation and Generalizability Response: We have now implemented 10-fold cross-validation to assess model performance more rigorously. The cross-validation results show: Cross-validated accuracy: 68.5% (±3.8%) Cross-validated sensitivity: 46.1% (±5.2%) Cross-validated specificity: 82.4% (±4.1%) Area Under Curve (AUC): 0.642 (±0.045) These metrics provide a more realistic and robust estimate of model performance and demonstrate good generalizability across different data partitions. Comment 2: Definition and Operationalization of Outcome Variable Response: We have clarified the binary outcome definition in the Methods section. The "successful transition" was operationalized using a composite satisfaction score approach: Successful transition: Mean satisfaction score ≥ 3.5 across benefits and welfare domains Unsuccessful transition: Mean satisfaction score < 3.5 Final distribution: Successful transitions (n=195, 45.3%), Unsuccessful transitions (n=235, 54.7%) This threshold was determined through ROC analysis to optimize the sensitivity-specificity balance for practical application. Comment 3: Handling of Potential Class Imbalance Response: Our analysis revealed moderate class imbalance (45.3% vs 54.7%). We implemented Synthetic Minority Oversampling Technique (SMOTE) to address this issue, resulting in improved performance: Improved sensitivity: 54.3% (±4.8%) Maintained specificity: 78.9% (±4.5%) Overall accuracy: 66.8% (±4.2%) Enhanced AUC: 0.666 (±0.042) Comment 4: Interpretability of the SVM Model Response: We conducted comparative analysis with other interpretable models to justify SVM selection: Logistic Regression: 66.2% (±4.0%) accuracy Decision Trees: 63.1% (±4.8%) accuracy Random Forest: 65.8% (±4.5%) accuracy Gradient Boosting: 65.1% (±4.3%) accuracy The linear SVM's superior cross-validated performance (68.5%) combined with interpretable feature weights justifies its selection over alternative approaches. Comment 5: Strengthening Contextual Discussion Response: We have substantially expanded the discussion to include: Alignment with Thailand's 2019 National Health Security Act Connection to Ministry of Public Health's 2023-2027 Strategic Plan International comparisons with Brazil, Kenya, and Indonesia decentralization experiences Specific policy recommendations based on identified predictors Minor Comments All minor comments have been addressed: Abstract now includes cross-validated performance metrics Introduction includes comprehensive SVM selection rationale Added Table 4 (confusion matrix) and Figure 1 (ROC curve analysis) Strengthened demographic predictor evidence with additional international citations Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: Model Accuracy (%) Sensitivity (%) Specificity (%) AUC Linear SVM 68.5 ± 3.8 46.1 ± 5.2 82.4 ± 4.1 0.642 Logistic Regression 66.2 ± 4.0 43.8 ± 5.0 81.1 ± 4.3 0.625 Random Forest 65.8 ± 4.5 48.9 ± 5.8 77.2 ± 4.8 0.631 Gradient Boosting 65.1 ± 4.3 47.2 ± 5.5 78.5 ± 4.2 0.628 Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) and Figure 2 (feature importance plot) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Summary of Manuscript Revisions New Sections Added: Cross-Validation Methodology (Methods section) Comparative Model Analysis (Results section) Enhanced Policy Context (Discussion section) Comprehensive Limitations (Discussion section) New Tables and Figures: Table 3: Cross-validation performance comparison Table 4: Confusion matrix for optimal SVM model Figure 1: ROC curve analysis with AUC comparisons Figure 2: Feature importance visualization with confidence intervals Enhanced Content: Methods: Added 756 words covering validation procedures Results: Added 445 words with cross-validation findings Discussion: Added 623 words with policy context and international comparisons References: Added 8 new citations including all suggested sources Key Methodological Improvements: Rigorous 10-fold cross-validation implementation Class imbalance handling through SMOTE Hyperparameter optimization documentation Comparative model benchmarking Feature stability assessment Enhanced interpretability analysis Policy Relevance Enhancement: Direct alignment with Thailand's health policy framework International contextualization with comparable systems Evidence-based recommendations for implementation Quantitative guidance for resource allocation Technical Quality Improvements: Standardized mathematical notation Corrected typographical errors Enhanced data transparency Improved statistical reporting standards We believe these comprehensive revisions have significantly strengthened the manuscript's methodological rigor while maintaining its practical relevance for healthcare workforce management in decentralization contexts. The study now provides robust evidence for evidence-based policy development and implementation. Sincerely, Chinakorn Sujimongkol Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below are our detailed responses to each of your major and minor comments: Major Comments Comment 1: Model Validation and Generalizability Response: We have now implemented 10-fold cross-validation to assess model performance more rigorously. The cross-validation results show: Cross-validated accuracy: 68.5% (±3.8%) Cross-validated sensitivity: 46.1% (±5.2%) Cross-validated specificity: 82.4% (±4.1%) Area Under Curve (AUC): 0.642 (±0.045) These metrics provide a more realistic and robust estimate of model performance and demonstrate good generalizability across different data partitions. Comment 2: Definition and Operationalization of Outcome Variable Response: We have clarified the binary outcome definition in the Methods section. The "successful transition" was operationalized using a composite satisfaction score approach: Successful transition: Mean satisfaction score ≥ 3.5 across benefits and welfare domains Unsuccessful transition: Mean satisfaction score < 3.5 Final distribution: Successful transitions (n=195, 45.3%), Unsuccessful transitions (n=235, 54.7%) This threshold was determined through ROC analysis to optimize the sensitivity-specificity balance for practical application. Comment 3: Handling of Potential Class Imbalance Response: Our analysis revealed moderate class imbalance (45.3% vs 54.7%). We implemented Synthetic Minority Oversampling Technique (SMOTE) to address this issue, resulting in improved performance: Improved sensitivity: 54.3% (±4.8%) Maintained specificity: 78.9% (±4.5%) Overall accuracy: 66.8% (±4.2%) Enhanced AUC: 0.666 (±0.042) Comment 4: Interpretability of the SVM Model Response: We conducted comparative analysis with other interpretable models to justify SVM selection: Logistic Regression: 66.2% (±4.0%) accuracy Decision Trees: 63.1% (±4.8%) accuracy Random Forest: 65.8% (±4.5%) accuracy Gradient Boosting: 65.1% (±4.3%) accuracy The linear SVM's superior cross-validated performance (68.5%) combined with interpretable feature weights justifies its selection over alternative approaches. Comment 5: Strengthening Contextual Discussion Response: We have substantially expanded the discussion to include: Alignment with Thailand's 2019 National Health Security Act Connection to Ministry of Public Health's 2023-2027 Strategic Plan International comparisons with Brazil, Kenya, and Indonesia decentralization experiences Specific policy recommendations based on identified predictors Minor Comments All minor comments have been addressed: Abstract now includes cross-validated performance metrics Introduction includes comprehensive SVM selection rationale Added Table 4 (confusion matrix) and Figure 1 (ROC curve analysis) Strengthened demographic predictor evidence with additional international citations Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: Model Accuracy (%) Sensitivity (%) Specificity (%) AUC Linear SVM 68.5 ± 3.8 46.1 ± 5.2 82.4 ± 4.1 0.642 Logistic Regression 66.2 ± 4.0 43.8 ± 5.0 81.1 ± 4.3 0.625 Random Forest 65.8 ± 4.5 48.9 ± 5.8 77.2 ± 4.8 0.631 Gradient Boosting 65.1 ± 4.3 47.2 ± 5.5 78.5 ± 4.2 0.628 Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) and Figure 2 (feature importance plot) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Summary of Manuscript Revisions New Sections Added: Cross-Validation Methodology (Methods section) Comparative Model Analysis (Results section) Enhanced Policy Context (Discussion section) Comprehensive Limitations (Discussion section) New Tables and Figures: Table 3: Cross-validation performance comparison Table 4: Confusion matrix for optimal SVM model Figure 1: ROC curve analysis with AUC comparisons Figure 2: Feature importance visualization with confidence intervals Enhanced Content: Methods: Added 756 words covering validation procedures Results: Added 445 words with cross-validation findings Discussion: Added 623 words with policy context and international comparisons References: Added 8 new citations including all suggested sources Key Methodological Improvements: Rigorous 10-fold cross-validation implementation Class imbalance handling through SMOTE Hyperparameter optimization documentation Comparative model benchmarking Feature stability assessment Enhanced interpretability analysis Policy Relevance Enhancement: Direct alignment with Thailand's health policy framework International contextualization with comparable systems Evidence-based recommendations for implementation Quantitative guidance for resource allocation Technical Quality Improvements: Standardized mathematical notation Corrected typographical errors Enhanced data transparency Improved statistical reporting standards We believe these comprehensive revisions have significantly strengthened the manuscript's methodological rigor while maintaining its practical relevance for healthcare workforce management in decentralization contexts. The study now provides robust evidence for evidence-based policy development and implementation. Sincerely, Chinakorn Sujimongkol Competing Interests: The authors declare no competing interests Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 09 Jan 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 2 (revision) 24 Sep 25 read read read Version 1 09 Jan 25 read read Manaporn Chatchumni , Rangsit University, Lak Hok, Thailand Thulasi Bikku , Amrita School of Computing Amaravati, Amrita Vishwa, Vidyapeetham, Amaravati, India Ali Husnain , Chicago State University, Chicago, USA Franklin Akwasi Adjei , University of Wyoming, Wyoming, USA 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 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Adjei F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Nov 2025 | for Version 2 Franklin Akwasi Adjei , University of Wyoming, Wyoming, USA 0 Views copyright © 2025 Adjei F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Statistical Analysis and Methodology Appropriateness of Statistical Methods The statistical analysis in this study is effectively implemented, especially the use of the Support Vector Machine for predicting healthcare workforce transitions. The choice of SVM is appropriate given its strength in handling complex, multidimensional data, which is crucial for predicting workforce outcomes influenced by multiple interrelated factors (e.g., compensation, career development, education). The study uses cross-validation to assess the model’s performance, which is essential in machine learning to prevent overfitting and ensure that the results generalize well to unseen data. The cross-validated accuracy of 68.5% ± 3.8% is a reasonable result for a healthcare workforce prediction model and aligns with typical performance levels reported in machine learning studies in healthcare (Lee et al., 2022). However, it would be helpful to include a bit more detail about what accuracy levels are considered acceptable when healthcare personnel make transitions. This can make the discussion clearer and more comprehensive. Given the consequences of misclassifying transitions, it would be useful to emphasize further the trade-offs among accuracy, sensitivity, and specificity in healthcare applications. While the sample size (430 participants) appears sufficient, a detailed power analysis could provide greater clarity for the study. This would provide more confidence in the sample size's ability to detect significant predictors and clarify whether the study is underpowered or well-powered. 2. Presentation and Clarity The manuscript is thoughtfully organized, featuring a clear introduction, methods, results, and discussion that guide readers smoothly through the research. The structure makes it easy to follow and understand the study's progression sections. The authors provide a comprehensive overview of the study's objectives and methodology, followed by a detailed analysis of the findings. However, some sections could benefit from further clarification and elaboration. The introduction sets the context well, but it could improve by explaining the specific advantages of using SVMs for predicting workforce transitions, particularly compared to traditional statistical methods. A more explicit discussion of the limitations of traditional methods (e.g., logistic regression) and why SVM offers a better solution in this context would enhance the manuscript’s clarity. 3. Discussion and Interpretation of Results The SVM analysis strongly supports the study's findings that competitive pay, career advancement, and fair promotions are key factors in successful workforce transitions. These findings are consistent with existing research on employee retention, highlighting the importance of financial incentives and professional growth in healthcare workforce mobility. Notably, education level emerged as the sole influential demographic factor; however, the authors should be aware of possible bias due to the higher proportion of bachelor’s degree holders in the sample. This imbalance could restrict the generalizability of the results to the wider workforce. Future studies should employ stratified sampling to ensure a more representative demographic distribution. 4. Practical Implications and Generalizability The authors should discuss the potential limitations of applying the model outside of Thailand, given that decentralization processes and healthcare systems vary widely across countries. Multi-country studies would strengthen the external validity of the findings. The authors could also discuss how the model’s findings could inform broader healthcare policy decisions, such as the structuring of decentralized healthcare systems in low- and middle-income countries. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Health Research, Public Health I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Adjei FA. Peer Review Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.187886.r425329) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-49/v2#referee-response-425329 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Husnain A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 Oct 2025 | for Version 2 Ali Husnain , Chicago State University, Chicago, Illinois, USA 0 Views copyright © 2025 Husnain A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The revised manuscript presents a well-executed and meaningful application of Support Vector Machine modeling to predict healthcare workforce transition success during Thailand’s decentralization process. The study stands out for its combination of methodological rigor and practical relevance. By using a cross-sectional design with 10-fold cross-validation and SMOTE resampling, the authors demonstrate an advanced yet transparent approach to predictive modeling in a policy context that has often relied on descriptive analyses. The introduction establishes a clear research gap and positions the study within current decentralization and workforce management literature. The methods section is detailed, outlining sampling strategies, variable definitions, and model comparison across linear and nonlinear kernels. The technical enhancements added in this version—particularly cross-validation, benchmarking against logistic regression and ensemble models, and the inclusion of AUC and F1-score metrics—strengthen the paper considerably. Results are presented with appropriate balance between quantitative reporting and interpretive discussion. The identification of compensation and professional development as key predictors is both statistically grounded and theoretically sound. The discussion effectively links findings to Thailand’s current health policy framework and comparable international experiences, giving the study broader relevance. There are, however, minor areas for improvement. Full reproducibility remains limited because the raw dataset cannot be openly shared; the authors could consider publishing de-identified or synthetic data to facilitate replication. A brief description of hyperparameter tuning beyond the C parameter would also enhance transparency. Finally, the discussion could be slightly condensed to reduce repetition and improve flow. Overall, this is a high-quality, policy-relevant study that meets publication standards. It contributes meaningful insights to the intersection of healthcare workforce research and machine learning and demonstrates thoughtful methodological execution. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Data Science, Machine Learning in Healthcare, Health Informatics, Predictive Modeling, and Public Health Systems Research. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Husnain A. Peer Review Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.187886.r425323) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-49/v2#referee-response-425323 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Chatchumni M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Oct 2025 | for Version 2 Manaporn Chatchumni , Rangsit University, Lak Hok, Pathum Thani, Thailand 0 Views copyright © 2025 Chatchumni M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have carefully reviewed the revised version of the manuscript titled “Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization” by Sarakshetrin et al. The authors have satisfactorily addressed all previous comments, with significant improvements in the methodological rigor, clarity of variable definitions, model validation, and contextual discussion. The integration of national and international policy perspectives, along with enhanced technical presentation, has notably strengthened the quality and relevance of the paper. Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Chatchumni M. Peer Review Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.187886.r417068) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-49/v2#referee-response-417068 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Bikku T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 12 Aug 2025 | for Version 1 Thulasi Bikku , Amrita School of Computing Amaravati, Amrita Vishwa, Vidyapeetham, Amaravati, Andhra Pradesh, India 0 Views copyright © 2025 Bikku T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Model Validation : Lack of train-test splitting or k-fold cross-validation undermines reported accuracy (71.43%). Implement and report 5- or 10-fold cross-validation. SVM Development : Unclear hyperparameter tuning (e.g., regularization parameter C) and feature weight threshold (>0.25). Provide details and justification. Sensitivity-Specificity Imbalance : Low sensitivity (49.02%) vs. high specificity (85.37%) needs deeper analysis and strategies to improve (e.g., class weight adjustments). Sampling Bias : Predominance of bachelor’s degree holders (71.16%) may bias education’s importance (coefficient = 0.236). Conduct stratified analysis and clarify province selection. Literature Comparison : Limited benchmarking against other predictive models (e.g., random forests). Include comparisons and integrate international decentralization studies. Clarity : Fix typographical errors (e.g., missing Table 3), standardize notation (e.g., ( f(x) )), and fully describe tables. Visualizations : Add figures (e.g., feature importance plot, ROC curve) for clarity. Data Transparency : Clarify data availability and consent process to align with open science. Limitations : Expand discussion on cross-sectional design limitations and generalizability beyond Thailand. Implement k-fold cross-validation to validate model performance. Clarify hyperparameter tuning and feature weight threshold rationale. Analyze low sensitivity and explore improvement strategies. Address sampling bias with stratified analysis and detail province selection. Benchmark against other models and integrate global decentralization studies. Correct errors, standardize notation, add visualizations, and clarify data availability. Add References: Bikku, Thulasi, and KPNV Satya Sree. "Deep learning approaches for classifying data: a review." Journal of Engineering Science and Technology 15.4 (2020): 2580-2594. Bikku, Thulasi. "Multi-layered deep learning perceptron approach for health risk prediction." Journal of Big Data 7.1 (2020): 50. BIKKU, THULASI, et al. "Healthcare Biclustering of Predictive Gene Expression Using LSTM Based Support Vector Machine." Informing Science 28 (2025): 12. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly References 1. Bikku, Thulasi, and KPNV Satya Sree. "Deep learning approaches for classifying data: a review." Journal of Engineering Science and Technology 15.4 (2020): 2580-2594. 2. Bikku, Thulasi. "Multi-layered deep learning perceptron approach for health risk prediction." Journal of Big Data 7.1 (2020): 50. BIKKU, THULASI, et al. "Healthcare Biclustering of Predictive Gene Expression Using LSTM Based Support Vector Machine." Informing Science 28 (2025): 12. Competing Interests No competing interests were disclosed. Reviewer Expertise Bioinformatics, Deep Learning, Quantum Computing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Chinakorn Sujimongkol, Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: We have conducted comprehensive benchmarking against alternative algorithms, including Logistic Regression, Random Forest, and Gradient Boosting, in addition to Linear SVM. The comparative results of model accuracy, sensitivity, specificity, and AUC are summarized in Table 3 (Cross-validation performance comparison) in the revised manuscript. In addition, the detailed classification outcomes for the optimal SVM model are presented in Table 4 (Confusion matrix for optimal SVM model) . Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Sincerely, Chinakorn Sujimongkol Corresponding author View more View less Competing Interests The authors declare no competing interests reply Respond Report a concern Bikku T. Peer Review Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r393051) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-393051 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Chatchumni M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 28 Mar 2025 | for Version 1 Manaporn Chatchumni , Rangsit University, Lak Hok, Pathum Thani, Thailand 0 Views copyright © 2025 Chatchumni M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Editors, Thank you for the opportunity to review this manuscript. The study presents a novel and relevant application of machine learning—specifically Support Vector Machine (SVM)—to model the predictors of healthcare workforce transition success in the context of decentralization in Thailand. This topic is timely and significant, offering valuable insights into both public health workforce planning and AI-driven predictive analytics. While the manuscript is well-structured and contributes to a growing area of applied machine learning in health systems research, I offer the following major and minor comments to enhance its clarity, methodological rigor, and policy relevance: Major Comments 1. Model Validation and Generalizability The current manuscript lacks any mention of model validation (e.g., train-test split or cross-validation), which limits the credibility of the reported performance metrics (accuracy: 71.43%). Recommendation: Incorporate k-fold cross-validation or a separate test set to assess model generalizability and reduce potential overfitting. 2. Definition and Operationalization of Outcome Variable The manuscript references “successful transition” based on satisfaction levels, but the criteria for classification into “success” or “non-success” are not clearly defined. Recommendation: Provide a more explicit explanation of how the binary outcome was coded, including any threshold values or composite score criteria. 3. Handling of Potential Class Imbalance The reported sensitivity (49.02%) suggests potential imbalance in the outcome classes. Recommendation: Report the distribution of successful vs. unsuccessful cases and consider using resampling or weighting methods (e.g., SMOTE, class weights) to improve sensitivity. 4. Interpretability of the SVM Model While linear SVM was chosen, its interpretability compared to other models (e.g., logistic regression or decision trees) could be discussed more thoroughly. Recommendation: Justify the use of a linear kernel over other interpretable models and elaborate on the reliability of the identified feature weights as indicators of predictor importance. 5. Strengthening Contextual Discussion The findings could be better connected to Thailand’s decentralization reform context. Recommendation: Expand the discussion on how these predictors align with current national health workforce policies and potential implications for policy adaptation or intervention design. Minor Comments • Abstract: Consider reporting key model performance metrics in the abstract for transparency. • Introduction: A brief rationale for selecting SVM over other machine learning models would strengthen the justification. • Tables/Figures: A confusion matrix or ROC curve figure would enhance understanding of model performance. • References: Where possible, provide additional evidence to support the unique influence of demographic predictors like education. Overall Assessment This manuscript is a valuable contribution to the intersection of AI and health systems research. It is particularly relevant for policymakers and administrators navigating workforce transitions under decentralization. With the suggested revisions, the paper will meet a higher standard of methodological transparency and policy relevance. Sincerely, Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise This manuscript is a valuable contribution to the intersection of AI and health systems research. It is particularly relevant for policymakers and administrators navigating workforce transitions under decentralization. With the suggested revisions, the paper will meet a higher standard of methodological transparency and policy relevance. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 10 Sep 2025 Chinakorn Sujimongkol, Faculty of Public Health and Allied Health Sciences, Praboromarajchanok Institute, Nonthaburi, Thailand Response to Reviewers Response to Reviewer 1 Dear Reviewer 1, We sincerely appreciate your thorough review and constructive feedback. Your comments have significantly improved the quality of our manuscript. Below are our detailed responses to each of your major and minor comments: Major Comments Comment 1: Model Validation and Generalizability Response: We have now implemented 10-fold cross-validation to assess model performance more rigorously. The cross-validation results show: Cross-validated accuracy: 68.5% (±3.8%) Cross-validated sensitivity: 46.1% (±5.2%) Cross-validated specificity: 82.4% (±4.1%) Area Under Curve (AUC): 0.642 (±0.045) These metrics provide a more realistic and robust estimate of model performance and demonstrate good generalizability across different data partitions. Comment 2: Definition and Operationalization of Outcome Variable Response: We have clarified the binary outcome definition in the Methods section. The "successful transition" was operationalized using a composite satisfaction score approach: Successful transition: Mean satisfaction score ≥ 3.5 across benefits and welfare domains Unsuccessful transition: Mean satisfaction score < 3.5 Final distribution: Successful transitions (n=195, 45.3%), Unsuccessful transitions (n=235, 54.7%) This threshold was determined through ROC analysis to optimize the sensitivity-specificity balance for practical application. Comment 3: Handling of Potential Class Imbalance Response: Our analysis revealed moderate class imbalance (45.3% vs 54.7%). We implemented Synthetic Minority Oversampling Technique (SMOTE) to address this issue, resulting in improved performance: Improved sensitivity: 54.3% (±4.8%) Maintained specificity: 78.9% (±4.5%) Overall accuracy: 66.8% (±4.2%) Enhanced AUC: 0.666 (±0.042) Comment 4: Interpretability of the SVM Model Response: We conducted comparative analysis with other interpretable models to justify SVM selection: Logistic Regression: 66.2% (±4.0%) accuracy Decision Trees: 63.1% (±4.8%) accuracy Random Forest: 65.8% (±4.5%) accuracy Gradient Boosting: 65.1% (±4.3%) accuracy The linear SVM's superior cross-validated performance (68.5%) combined with interpretable feature weights justifies its selection over alternative approaches. Comment 5: Strengthening Contextual Discussion Response: We have substantially expanded the discussion to include: Alignment with Thailand's 2019 National Health Security Act Connection to Ministry of Public Health's 2023-2027 Strategic Plan International comparisons with Brazil, Kenya, and Indonesia decentralization experiences Specific policy recommendations based on identified predictors Minor Comments All minor comments have been addressed: Abstract now includes cross-validated performance metrics Introduction includes comprehensive SVM selection rationale Added Table 4 (confusion matrix) and Figure 1 (ROC curve analysis) Strengthened demographic predictor evidence with additional international citations Response to Reviewer 2 Dear Reviewer 2, Thank you for your comprehensive feedback and the detailed list of recommendations. We have systematically addressed each of your concerns: Responses to Specific Points Points 1, 10-12: Model Validation Implementation We have completed rigorous 10-fold cross-validation analysis revealing: Cross-validated accuracy: 68.5±3.8% Optimal hyperparameter: C=1.0 (determined through grid search) Feature weight threshold >0.25 justified through variance stabilization analysis Improved sensitivity through SMOTE implementation: 54.3±4.8% Points 4, 13: Sampling Bias Assessment We conducted stratified analysis by education level and detailed province selection methodology: Educational bias acknowledged with coefficient of variation analysis Province selection criteria now explicitly detailed with demographic characteristics Sensitivity analysis performed excluding education as predictor Points 5, 14: Comparative Model Analysis Comprehensive benchmarking against alternative algorithms completed: Model Accuracy (%) Sensitivity (%) Specificity (%) AUC Linear SVM 68.5 ± 3.8 46.1 ± 5.2 82.4 ± 4.1 0.642 Logistic Regression 66.2 ± 4.0 43.8 ± 5.0 81.1 ± 4.3 0.625 Random Forest 65.8 ± 4.5 48.9 ± 5.8 77.2 ± 4.8 0.631 Gradient Boosting 65.1 ± 4.3 47.2 ± 5.5 78.5 ± 4.2 0.628 Points 6-9, 15: Technical Corrections Mathematical notation standardized: f(x) = sign(∑ᵢ₌₁ⁿ αᵢyᵢK(xᵢ,x) + b) Added Table 4 (confusion matrix) and Figure 2 (feature importance plot) Fixed all typographical errors Enhanced data availability statement Point 16: Literature Integration All three suggested references have been incorporated: Bikku & Sree (2020): Integrated into methodology discussion on classification approaches Bikku (2020): Referenced in healthcare prediction context and risk assessment Bikku et al. (2025): Cited for advanced healthcare ML applications and comparison Detailed Technical Improvements Cross-Validation Implementation: The 10-fold cross-validation maintained stratified sampling to preserve class distribution across folds. Performance stability was assessed through coefficient of variation analysis, with all top predictors showing CV <0.15, indicating robust feature importance rankings. Class Imbalance Resolution: SMOTE implementation during training phases generated synthetic minority class examples, improving model balance. The trade-off between sensitivity and specificity was optimized for healthcare decision-making contexts, where identifying successful transitions is crucial for workforce planning. Feature Stability Analysis: Cross-validation confirmed the consistency of our top 5 predictors: Competitive compensation: 0.427 (CV range: 0.398-0.456) Career development: 0.358 (CV range: 0.334-0.382) Fair promotion: 0.336 (CV range: 0.312-0.360) Hazardous work compensation: 0.285 (CV range: 0.265-0.305) Educational leave: 0.252 (CV range: 0.235-0.269) Summary of Manuscript Revisions New Sections Added: Cross-Validation Methodology (Methods section) Comparative Model Analysis (Results section) Enhanced Policy Context (Discussion section) Comprehensive Limitations (Discussion section) New Tables and Figures: Table 3: Cross-validation performance comparison Table 4: Confusion matrix for optimal SVM model Figure 1: ROC curve analysis with AUC comparisons Figure 2: Feature importance visualization with confidence intervals Enhanced Content: Methods: Added 756 words covering validation procedures Results: Added 445 words with cross-validation findings Discussion: Added 623 words with policy context and international comparisons References: Added 8 new citations including all suggested sources Key Methodological Improvements: Rigorous 10-fold cross-validation implementation Class imbalance handling through SMOTE Hyperparameter optimization documentation Comparative model benchmarking Feature stability assessment Enhanced interpretability analysis Policy Relevance Enhancement: Direct alignment with Thailand's health policy framework International contextualization with comparable systems Evidence-based recommendations for implementation Quantitative guidance for resource allocation Technical Quality Improvements: Standardized mathematical notation Corrected typographical errors Enhanced data transparency Improved statistical reporting standards We believe these comprehensive revisions have significantly strengthened the manuscript's methodological rigor while maintaining its practical relevance for healthcare workforce management in decentralization contexts. The study now provides robust evidence for evidence-based policy development and implementation. Sincerely, Chinakorn Sujimongkol View more View less Competing Interests The authors declare no competing interests reply Respond Report a concern Chatchumni M. Peer Review Report For: Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization [version 1; peer review: 2 approved with reservations] . F1000Research 2025, 14 :49 ( https://doi.org/10.5256/f1000research.176268.r358598) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-49/v1#referee-response-358598 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 = "Support Vector Machine-Based Prediction Model...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-49/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-49/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-49/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Sarakshetrin 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/14-49/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/14-49", templates : { twitter : "Support Vector Machine-Based Prediction Model for Healthcare.... Sarakshetrin A et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-49/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/160378/176268") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "176268"); $(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 = { "418438": 0, "418439": 0, "418436": 0, "418437": 0, "418434": 0, "418435": 0, "418432": 0, "418433": 0, "359845": 0, "359844": 0, "359841": 0, "359840": 0, "359843": 0, "359842": 0, "417068": 3, "417067": 0, "394814": 0, "394815": 0, "394812": 0, "394813": 0, "394810": 0, "394811": 0, "394808": 0, "394809": 0, "358599": 0, "358598": 18, "394816": 0, "394817": 0, "358601": 0, "358600": 0, "393045": 0, "393044": 0, "393047": 0, "393046": 0, "393043": 0, "393042": 0, "393049": 0, "393048": 0, "393051": 9, "393050": 0, "425326": 0, "425327": 0, "425324": 0, "425325": 0, "425322": 0, "425323": 2, "425320": 0, "425321": 0, "425328": 0, "425329": 3, "418430": 0, "418431": 0, "418429": 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 = "e1b62c47-b834-48b9-a12e-0de95cfd6a72"; 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(",","/")); } }); });