Performance Data Advocacy for Continuing Professional Development in Health Professions
Bibliographic record
Abstract
ABSTRACT: Efforts to optimize continuing professional development (CPD) are ongoing and include advocacy for the use of clinician performance data. Several educational and quality-based frameworks support the use of performance data to achieve intended improvement outcomes. Although intuitively appealing, the role of performance data for CPD has been uncertain and its utility mainly assumed. In this Scholarly Perspective, the authors briefly review and trace arguments that have led to the conclusion that performance data are essential for CPD. In addition, they summarize and synthesize a recent and ongoing research program exploring the relationship physicians have with performance data. They draw on Collins, Onwuegbuzie, and Johnson's legitimacy model and Dixon-Woods' integrative approach to generate inferences and ways of moving forward. This interpretive approach encourages questioning or raising of assumptions about related concepts and draws on the perspectives (i.e., interpretive work) of the research team to identify the most salient points to guide future work. The authors identify 6 stimuli for future programs of research intended to support broader and better integration of performance data for CPD. Their aims are to contribute to the discourse on data advocacy for CPD by linking conceptual, methodologic, and analytic processes and to stimulate discussion on how to proceed on the issue of performance data for CPD purposes. They hope to move the field from a discussion on the utility of data for CPD to deeper integration of relevant conceptual frameworks.
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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 | 0.225 | 0.420 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".