The Importance of Evaluating New Models of Care to Better Meet Patient Needs
Bibliographic record
Abstract
In their paper "Using evidence to meet population healthcare needs: successes and challenges," Tomblin Murphy and MacKenzie highlight the critically important need to shift health system reform efforts from those focused on the more salient provider or supply side to those that more appropriately attempt to better meet patient and population health needs. A population needs-based focus on health workforce planning that is more than just rhetoric is indeed important, as is acknowledging that population health needs are best addressed through an interdisciplinary approach to care. Most importantly, the authors also argue that rigorous evaluation is needed to scale up the most promising health workforce innovations: this could be best addressed with a dedicated arm's-length health workforce evidence infrastructure.
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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 | 0.136 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.073 | 0.086 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".