Of Honey and Health Policy: The Limits of Sweet, Sticky Substances in Reforming Primary Care
Bibliographic record
Abstract
It is a well-known axiom that one attracts more flies with honey than vinegar. Nowhere has this approach been taken more to heart than in the past decade of primary care policy in Canada. Governments, physician and nursing organizations and regional health authorities have invested in a lot of "honey" to draw healthcare providers onto a path from single-physician offices to team-based care with flexible hours and a population-based approach. In the lead essay for this edition of Healthcare Papers, Kates and colleagues have outlined a framework that embraces this paradigm. Their articulation of a framework is a place to start, but it can only be a start. To make that framework come alive, a wider variety of policy tools will be needed than have been used thus far, and by a wider variety of actors. Within the healthcare workforce itself, leadership, vision and the courage to hold ourselves to account for changes to primary care are needed.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.076 | 0.074 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".