Beyond greener pastures: exploring contexts surrounding Filipino nurse migration in Canada through oral history
Bibliographic record
Abstract
The history of immigrant Filipino nurses in Canada has received little attention, yet Canada is a major receiving country of a growing number of Filipino migrants and incorporates Filipino immigrant nurses into its healthcare workforce at a steady rate. This study aims to look beyond the traditional economic and policy analysis perspectives of global migration and beyond the push and pull factors commonly discussed in the migration literature. Through oral history, this study explores biographical histories of nine Filipino immigrant nurses currently working in British Columbia and Alberta, Canada. Narratives reveal the instrumental role of the deeply embedded culture of migration in the Philippines in influencing Filipino nurses to migrate. Additionally, the stories illustrate the weight of cultural pressures and societal constructs these nurses faced that first colored their decision to pursue a career in nursing and ultimately to pursue emigration. Oral history is a powerful tool for examining migration history and sheds light on nuances of experience that might otherwise be neglected. This study explores the complex connections between various factors motivating Filipino nurse migration, the decision-making process, and other pre-migration experiences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".