Health worker migration in Canada: Histories, geographies, and ethics (Working paper number 12-02)
Bibliographic record
Abstract
This working paper explores issues of health worker migration through examining the history, geography, and ethics of international recruitment and migration of health workers to Canada, focusing on the experiences of registered nurses from the Philippines. During the past few decades the migration of Filipino nurses to Canada has considerably expanded, with nurses from the Philippines making up the largest group of all immigrant nurses in the Canadian workforce. Derived from presentations, discussions, and insights from an interdisciplinary workshop on health worker migration attended by academics, professionals, policymakers and health workers, we underscore the importance of further debate on the issues confronting recent migrant nurses from the Philippines to Canada. The aim of this working paper is to bring the complexity of the experiences of migrating nurses in health care explored during the workshop through various lenses of transnational historical research and biographical reflection, contextual and local geographical studies, evolving ethical norms and policies guidelines around recruitment, national and internationally, to a wider audience. We call for more in-depth academic research engaging the perspectives of policymakers and health professionals and of migrant nurses affected by their decisions. Furthermore, we bring forward recommendations and insights raised during the workshop.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.045 | 0.017 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".