Bibliographic record
Abstract
Background: For years, Canada has benefited from the immigration of health care workers from Caribbean nations, which may have resulted in a service deficit in the source country; this is known as brain drain. In addition to health care service deficits, economical development in source countries such as Guyana may be stagnated by the loss of citizens with tertiary education. Objectives: We sought to identify experiences, attitudes, and push and pull factors pertaining to Guyanese health care workers who migrated to and studied and/or worked in Canada. Methods: A purposeful sample of 7 Guyanese health care worker expatriates now living in Canada was drawn from private networks. In-person and phone interviews were conducted with respondents. We applied content analysis to identify themes relating to respondents' motivations and experiences in migrating. Two researchers completed qualitative data analysis and discrepancies were resolved by consensus. Results: Push and pull themes identified include the existence of a champion who encouraged migration and/or retention, family connections, perceived responsibilities to country left behind, remuneration, opportunities for self and children, and most commonly opportunities for further education and career satisfaction based on merit. Conclusion: The desire of migrants to maintain constructive contact with the source country might be leveraged to empower capacity-building enterprises.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".