Health care workers and migrant health: Pre- and post-COVID-19 considerations for reviewing and expanding the research agenda
Bibliographic record
Abstract
The main purpose of this article is to review several ways in which health care workers could either impact migrant health or be directly impacted by migration and, based on this, suggest the expansion of the current research agenda on migration and health to address a range of topics that are currently either neglected, insufficiently researched, or researched from different perspectives. To ground this suggestion and emphasize the complexity and significance of migrant health research, we start by briefly reviewing several migration-related notions including the process of migration and its key facilitators and benefits; existing barriers to the provision of migrant health care; and the intricate links between health systems, health professionals, and migrant health. The three areas of research examined in this article address (i) the specific role of health workers in providing care to migrants and refugees and their capacity to do so, (ii) the health problems experienced by health workers who become migrants or refugees, and (iii) the precarious employment conditions experienced by both migrant and non-migrant health care workers. After summarizing the current available evidence on these topics, we discuss key information gaps and strategies to address them, while also incorporating several relevant COVID-19 pandemic considerations and research implications. Expanding the focus of research studies on migration and health could not only enhance the results of current strategies by supplying additional information to support their implementation but also spearhead the development of new solutions to the migrant health problem.
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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 | 0.076 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".