Motivations of nurses who migrate to <scp>C</scp>anada as domestic workers
Bibliographic record
Abstract
BACKGROUND: While some trained nurses migrate to destination countries to work as domestic workers, little is known about their migration motivations. AIM: This study explores the motivations of Philippine educated nurses who migrated to Canada through the Live-in Caregiver Program from 2001 to 2011 (a Canadian domestic worker programme). METHODS: A single case study qualitative methodology and the transnational feminist concept of global care chains were utilized for this study. Interviews of 15 Philippine educated nurses who migrated to Canada as domestic workers were conducted in the province of Ontario, Canada, between February to October 2012. All participants had a baccalaureate degree from the Philippines. Interviews were tape recorded, transcribed verbatim and analysed using critical discourse analysis, aided by NVIVO 10 data analysis software. RESULTS: Findings reveal a multi-step immigration process in which nurses migrate from the Philippines to the Middle East (especially Saudi Arabia) and finally to Canada. While emigration from the Philippines is mainly economically driven, migration from the Middle East to Canada is primarily motivated by the desire for Canadian citizenship for the family. Also, perceived social status and lifestyle in Canada as compared to the Middle East motivates this group of women to migrate to Canada. LIMITATION: The major limitation of this study is the lack of input from nursing policy makers. CONCLUSION: Gender-based familial ideologies and perspective on social status influence the migration decision of this group of nurses. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Implications for nursing and health policy makers include the provision of clear pre-migration information (including on the nursing registration process) to internationally educated nurses, advocacy for stronger immigration policies to ensure the integration of internationally educated nurses and a consideration of gender in all health human resource policies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".