Hiding in plain sight: the absence of consideration of the gendered dimensions in ‘source’ country perspectives on health worker migration
Bibliographic record
Abstract
BACKGROUND: Gender roles and relations affect both the drivers and experiences of health worker migration, yet policy responses rarely consider these gender dimensions. This lack of explicit attention from source country perspectives can lead to inadequate policy responses. METHODS: A Canadian-led research team partnered with co-investigators in the Philippines, South Africa, and India to examine the causes, consequences and policy responses to the international migration of health workers from these 'source' countries. Multiple-methods combined an initial documentary analysis, interviews and surveys with health workers and country-based stakeholders. We undertook an explicit gender-based analysis highlighting the gender-related influences and implications that emerged from the published literature and policy documents from the decade 2005 to 2015; in-depth interviews with 117 stakeholders; and surveys conducted with 3580 health workers. RESULTS: The documentary analysis of health worker emigration from South Africa, India and the Philippines reveal that gender can mediate access to and participation in health worker training, employment, and ultimately migration. Our analysis of survey data from nurses, physicians and other health workers in South Africa, India and the Philippines and interviews with policy stakeholders, however, reveals a curious absence of how gender might mediate health worker migration. Stereotypical views were evident amongst stakeholders; for example, in South Africa female health workers were described as "preferred" for "innate" personal characteristics and cultural reasons, and in India men are directed away from nursing roles particularly because they are considered only for women. The finding that inadequate remuneration was as a key migration driver amongst survey respondents in India and the Philippines, where nurses predominated in our sample, was not necessarily linked to underlying gender-based pay inequity. The documentary data suggest that migration may improve social status of female nurses, but it may also expose them to deskilling, as a result of the intersecting racism and sexism experienced in destination countries. Regardless of these underlying influences in migration decision-making, gender is rarely considered either as an important contextual influence or analytic category in the policy responses. CONCLUSION: An explicit gender-based analysis of health worker emigration, which may help to emphasize important equity considerations, could offer useful insights for the health and social policy responses adopted by source countries.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".