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Record W4310747593 · doi:10.1136/bmjopen-2022-068522

Understanding the trends, and drivers of emigration, migration intention and non-migration of health workers from low-income and middle-income countries: protocol for a systematic review

2022· review· en· W4310747593 on OpenAlexaff
Paul Ikhurionan, Yakubu Kevin Kwarshak, Ekhosuehi Theophilus Agho, Itua C G Akhirevbulu, Josephine Atat, Franca Erhiawarie, Emmanuel O Gbejewoh, Chinelo Iwegim, Ukachi C Nnawuihe, Uyoyo Odogu, Jermaine Okpere, Efe E Omoyibo, Efetobo Victor Orikpete, Uwaila Otakhoigbogie, Avwebo Ukueku, Patience Ugwi, Oghenebrume Wariri

Bibliographic record

VenueBMJ Open · 2022
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsFraser Health
Fundersnot available
KeywordsCINAHLMedicineProtocol (science)MEDLINECritical appraisalSystematic reviewEconomic shortageLow and middle income countriesGrey literatureDeveloping countryPathologyNursingAlternative medicineEconomic growthPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: The WHO estimates a shortage of 18 million health workers (HWs) by 2030, primarily in low-income and middle-income countries (LMICs). The perennial out-migration of HWs from LMICs, often to higher-income countries, further exacerbates the shortage. We propose a systematic review to understand the determinants of HWs out-migration, intention to migrate and non-migration from LMICs. METHODS AND ANALYSIS: This protocol was designed in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols guideline for the development and reporting of systematic review protocols. We will include English and French language primary studies (quantitative or qualitative) focused on any category of HWs; from any LMICs; assessed migration or intention to migrate; and reported any determinant of migration. A three-step search strategy that involves a search of one electronic database to refine the preliminary strategy, a full search of all included databases and reference list search of included full-text papers for additional articles will be employed. We will search Ovid MEDLINE, EMBASE, CINAHL, Global Health and Web of Science from inception to August 2022. The retrieved titles will be imported to EndNote and deduplicated. Two reviewers will independently screen all titles and abstract for eligibility using Rayyan. Risk of bias of the individual studies will be determined using the National Institute of Health study quality assessment tools for quantitative studies and the 10-item Critical Appraisal Skills Programme checklists for qualitative studies. The results will be presented in the form of narrative synthesis using a descriptive approach ETHICS AND DISSEMINATION: We will not seek ethical approval from an institutional review board, as this is a systematic review. At completion, we will submit the report of this review to a peer-reviewed journal for publication. Key findings will be presented at local and international conferences. PROSPERO REGISTRATION NUMBER: CRD42022334283.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.084
metaresearch head score (Gemma)0.126
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.084
Threshold uncertainty score0.442

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0840.126
Meta-epidemiology (narrow)0.0070.006
Meta-epidemiology (broad)0.0220.023
Bibliometrics0.0140.013
Science and technology studies0.0040.006
Scholarly communication0.0090.011
Open science0.0050.005
Research integrity0.0100.007
Insufficient payload (model declined to judge)0.0540.007

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.

Opus teacher head0.307
GPT teacher head0.537
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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