Not on the radar: the impact of rural health realities on Canadian public policy and HHR migration from Sub-Saharan Africa.
Bibliographic record
Abstract
The policy environment of Health Human Resource (HHR) demands in rural and remote areas of Canada seems to compel health system planners either to ignore or only pay lip service to memoranda of understanding and other non-binding international agreements on ethical principles of recruitment. Despite common acknowledgement that the migration of health professionals from Sub-Saharan Africa (SSA) and the resultant loss of capacity to deliver health services are devastating for populations in that region, Canadian public policy consideration of the "brain drain" of health human resources from SSA seems cursory, at best. As a result, broadly based domestic HHR policies and international development policy objectives invariably seem to conflict and produce unsatisfactory results that continue to be detrimental to populations of source countries in the developing world, while doing little to alleviate the continued reliance of Canada's rural and remote "'gateways" on foreign-trained health professionals. A key challenge for Canadian public policy, at all levels of government, is to coordinate and find common ground, whereby brain drain issues and specific domestic Canadian HHR needs can be simultaneously and effectively addressed. This research explored the congruity between rural HHR policy principles and international development objectives in the context of federal, provincial, and territorial government relations in Canada.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".