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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.097 | 0.135 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".