Planning for Canada's Health Workforce: Looking Back, Looking Forward
Bibliographic record
Abstract
"Are there enough health professionals in Canada, and will they be there when I need them? " Answers to these two seemingly simple questions cover a variety of complex and interrelated factors that are not fully understood, as the report about Canada's Healthcare Providers (CIHI 2001) makes clear. The report appears at a time when Canadian political leaders, healthcare organizations, caregivers and others involved with the healthcare system are looking for creative solutions to the human resources challenges facing the health system. Many of the issues are not new; over the last 50 years they have been raised by various groups and government commissions. But there is a sense of urgency today as options for renewing and sustaining Canada's health system are actively being explored. This essay offers highlights from the report, providing a portrait of what is known (and not known) about the people who work in healthcare across the country. It makes clear that whether there are (or are not) enough healthcare providers is not simply a question of numbers of health professionals. From changes in health and healthcare to shifts in the worklife and practice patterns of professionals, a better understanding of the wide range of factors affecting healthcare providers is essential to further the important debates taking place.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| 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".