Risk Based Regulation in Quality Assurance: Selection of (and Benefits Experienced by) Registrants Undertaking Regulator-mandated Peer Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose:. To identify risk and protective factors associated with physician performance in practice; to use this information to create a risk assessment scale; and, to test use of the risk assessment scale with a new population of assessed physicians.Design:. Physician assessments that were completed by community-based physicians between March 2016 and February 2022 (n =2708) were gathered to determine what professional characteristics and practice context factors were associated with poor peer practice assessment (PPA). The predictive capacity of the resulting model was then tested against a new sample of physician assessments completed between March 2022 and February 2023 (n =320).Results:. N=2401 physicians were eligible for inclusion in a logistic regression analysis, which resulted in an empirical model containing 11 variables that was able to account for 21.6% of the variance in the likelihood of receiving a poor PPA generated by the College of Physicians and Surgeons of British Columbia. The resulting model, when tested against 320 new cases, was able to predict good versus poor PPA performance with a sensitivity of 0.79 and specificity of 0.75. Not having undertaken peer review (OR=1.47) created a risk like that arising from a full decade passing since completion of medical school (OR=1.50).Conclusion:. In addition to being the largest known study of its type, this work builds on similar studies by demonstrating the capacity to use regulator-mandated peer review to empirically identify physicians who are at risk of substandard performance using factors that are safe from claims of violating Human Rights Codes; that emphasize modifiable aspects of practice; and that can be readily updated to account for change over time.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it