Influence of social media and the digital environment on international migration of health workforce from low- and middle-income countries post COVID-19 pandemic: a scoping review protocol
Notice bibliographique
Résumé
INTRODUCTION: Migration of the health workforce from low- and middle-income countries (LMCIs) is increasingly becoming a phenomenon of interest within migration governance systems. The COVID-19 pandemic aggravated health workforce shortages that have created job opportunities in high-income countries such as the USA, UK, Canada and Germany among others. Conditions of service in LMCIs are unattractive, leading to the search for better opportunities. The digital environment is becoming one of the facilitators of migration intentions due to the activities of recruitment agencies and the search for job opportunities on the World Wide Web. The digital environment creates opportunities for migration but also poses a security threat, economic loss and a brain drain to departure countries. However, there is a paucity of evidence on how the proliferation of advertisements on health workforce recruitment within social media, unsolicited emails and activities of recruitment agencies in the digital environment influence the migration of the health workforce and the implications of migration governance. METHOD AND ANALYSIS: This scoping review protocol describes a comprehensive systematic extraction and examination of existing literature to map key concepts and identify previous literature, noting the gaps in how social media and the digital environment are influencing the migration of the health workforce. We lean on Arksey and O'Malley's scoping framework in developing this protocol. This involves the following: identifying research questions, searching for the literature, selecting articles or studies, charting the data and organising and reporting the outcome of the review. The review question is informed by the population, concept and context framework, which details the population as the health workforce (doctors, nurses, midwives and pharmacists), the key concepts as migration, social media and digital environment, and the context as LMICs. The search strategy was developed with the assistance of an experienced librarian who will work with the team to conduct a Peer Review of Electronic Search Strategies to evaluate titles, abstracts and full-text articles for inclusion from databases such as Scopus, PubMed, MEDLINE and Google Scholar. Additionally, we will search grey literature sources including online news media, social media platforms (Facebook, Instagram and Twitter), web pages of WHO, UN and migration-related agencies, and interfaces like EBSCO host. Two members of the team will screen titles and abstracts, and all team members will screen full text for data extraction. Data from grey sources will be converted to transcripts, coded and grouped into themes and subthemes consistent with thematic analysis strategies. All authors will be involved in the synthesis of the data. We intend to follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines in reporting the outcome of peer-review sources. ETHICS AND DISSEMINATION: This is a scoping review protocol that addresses a subject of interest that poses no risk to individuals or groups. All the information will be retrieved from open sources only. The protocol was registered with the Open Science Framework registry (osf.oi/zan3q) to serve as an audit trail. Reports from the review will be published in peer-reviewed journals and presented at conferences.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,097 | 0,135 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,009 | 0,011 |
| Bibliométrie | 0,025 | 0,017 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,005 | 0,008 |
| Intégrité de la recherche | 0,008 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».