Workplace‐based assessment for general practitioners: using stakeholder perception to aid blueprinting of an assessment battery
Bibliographic record
Abstract
CONTEXT: The implementation of an assessment system may be facilitated by stakeholder agreement that appropriate qualities are being tested. This study investigated the extent to which stakeholders perceived 8 assessment formats (multiple-choice questions, objective structured clinical examination, video, significant event analysis, criterion audit, multi-source feedback, case analysis and patient satisfaction questionnaire) as able to assess varying qualities of doctors training in UK general practice. METHODS: Educationalists, general practice trainers and registrars completed a blueprinting style of exercise to rate the extent to which each evaluation format was perceived to assess each of 8 competencies derived primarily from the General Medical Council document 'Good Medical Practice'. RESULTS: There were high levels of agreement among stakeholders regarding the perceived qualities tested by the proposed formats (G = 0.82-0.93). Differences were found in participants' perceptions of how well qualities were able to be assessed and in the ability of the respective formats to test each quality. Multi-source feedback (MSF) was expected to assess a wide range of qualities, whereas Probity, Health and Ability to work with colleagues were limited in terms of how well they could be tested by the proposed formats. DISCUSSION: Awareness of the perceptions of stakeholders should facilitate the development and implementation of workplace-based assessment (WPBA) systems. These data shed light on the acceptability of various formats in a way that will inform further investigation of WPBA formats' validity and feasibility, while also providing evidence on which to base educational efforts regarding the value of each format.
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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 | 0.106 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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 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".