Geographic labour mobility and quality of life among healthcare workers in North America: a scoping review
Bibliographic record
Abstract
Objective To map the factors influencing labour mobility and the Quality of Life (QOL) domains among healthcare workers in North America. Study setting and design A scoping review was performed following the PRISMA-ScR guidelines. Data sources and analytic sample A literature search was conducted across eight databases: MEDLINE (Ovid), APA PsycINFO (Ovid), Embase (Ovid), Sociological Abstracts (ProQuest), Business Source Premier (EBSCO), International Bibliography of the Social Sciences (EBSCO), CINAHL (EBSCO), and Scopus (Elsevier), yielding 1,554 articles. Studies were included if they were empirical, focused on adults (18+) in North America who migrated for work, examined aspects of QOL or equivalent measures, and were published in English. The International Classification of Functioning, Disability, and Health (ICF) framework was used to structure the data synthesis. Principal findings Nine studies met the eligibility criteria, revealing three overarching themes on how labour mobility influences QOL from different perspectives: (1) Social Perspectives, (2) Functional Perspectives, and (3) Individual Perspectives. Conclusions The findings underscore the importance of social networks for health workers regarding job opportunities, emotional support, and career guidance. However, persistent discrimination, particularly regarding credential recognition and workplace integration, limits career advancement and mobility. Promoting inclusive work environments and equitable policies are crucial for enhancing workforce mobility and QOL among healthcare workers.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".