PERSPECTIVE: No person left behind: improving physician wellness in Canada
Bibliographic record
Abstract
Canadian health care costs are unsustainable and are among the highest in the world. A greater focus on system-level initiatives is needed, and recognizing physician wellness as a quality indicator for health care delivery may be part of the solution. Physicians’ psychosocial health is a significant cause for concern and has been directly tied to patient outcomes. However, suicide rates among physicians are approximately 2.5 times those of the general population and burnout rates are twice those of other workforces. Investing in physician health programs (PHPs), specifically the components dealing with psychosocial issues, is one way to make medicare sustainable. Further, greater provincial government support of national guidelines for the formation of PHPs is needed. This commentary focuses on these background issues and suggests a path toward a more sustainable health care strategy focusing on physician well-being.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".