Practicing Physicians' Needs for Assessment and Feedback as Part of Professional Development
Bibliographic record
Abstract
Recently, more is being learned about the linkages among assessment, feedback, and continued learning and professional development. The purpose of this article is to explore these linkages and to understand how assessment and feedback can guide professional development and related practice change. It includes a brief review of conceptual models that guide learning and practice change in general, related to both formally structured continuing professional development (CPD) sessions and to self-directed individual activities, and draws on these to inform learning and change from assessment and feedback. However, evidence and theory show that using assessment and feedback for learning and change are not naturally intuitive activities. We propose a 4-phase facilitated reflective process for enabling engagement with assessment data and feedback and using it for learning and change, and explore the varied personal and contextual factors which are influential and require consideration. We end with practical implications and suggestions.
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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 | 0.101 | 0.306 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".