MétaCan
Menu
Back to cohort
Record W2605214304 · doi:10.1186/s12960-017-0199-y

Causes, consequences, and policy responses to the migration of health workers: key findings from India

2017· article· en· W2605214304 on OpenAlexafffund
Margaret Walton‐Roberts, Vivien Runnels, S. Irudaya Rajan, Atul Sood, Sreelekha Nair, Philomina Thomas, Corinne Packer, Adrian MacKenzie, Gail Tomblin Murphy, Ronald Labonté, Ivy Lynn Bourgeault

Bibliographic record

VenueHuman Resources for Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsDalhousie UniversityUniversity of OttawaBalsillie School of International Affairs
FundersCanadian Institutes of Health Research
KeywordsWorkforceHealth policyHealth careMedicineHealth services researchGovernment (linguistics)Public healthHealth administrationNursingEnvironmental healthEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: This study sought to better understand the drivers of skilled health professional migration, its consequences, and the various strategies countries have employed to mitigate its negative impacts. The study was conducted in four countries-Jamaica, India, the Philippines, and South Africa-that have historically been "sources" of health workers migrating to other countries. The aim of this paper is to present the findings from the Indian portion of the study. METHODS: Data were collected using surveys of Indian generalist and specialist physicians, nurses, midwives, dentists, pharmacists, dieticians, and other allied health therapists. We also conducted structured interviews with key stakeholders representing government ministries, professional associations, regional health authorities, health care facilities, and educational institutions. Quantitative data were analyzed using descriptive statistics and regression models. Qualitative data were analyzed thematically. RESULTS: Shortages of health workers are evident in certain parts of India and in certain specialty areas, but the degree and nature of such shortages are difficult to determine due to the lack of evidence and health information. The relationship of such shortages to international migration is not clear. Policy responses to health worker migration are also similarly embedded in wider processes aimed at health workforce management, but overall, there is no clear policy agenda to manage health worker migration. Decision-makers in India present conflicting options about the need or desirability of curtailing migration. CONCLUSIONS: Consequences of health work migration on the Indian health care system are not easily discernable from other compounding factors. Research suggests that shortages of skilled health workers in India must be examined in relation to domestic policies on training, recruitment, and retention rather than viewed as a direct consequence of the international migration of health workers.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.477
Teacher spread0.377 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations97
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueHuman Resources for HealthSame topicGlobal Health Workforce IssuesFrench-language works237,207