A novel assessment of an evidence-based practice course using an authentic assignment
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is now a component of most medical curricula. Summative assessment instruments are often of debatable quality, do not cover the full spectrum of EBP or lack authenticity. AIM: To develop and evaluate the quality of an authentic assessment instrument for use in summative assessment of general practice trainees. METHODS: An assignment was designed based on the ask, acquire, appraise and apply steps of EBP. Content validity was evaluated by external EBP experts. Concurrent validity was tested with the Fresno test. Inter-rater agreement and internal consistency were measured. Acceptability and feasibility were also assessed. RESULTS: EBP experts agreed that the instrument had good content validity. Concurrent validity was good (disattenuated intraclass correlation coefficient 0.75). Inter-rater agreement varied from 0.70 to 0.83. Internal consistency was high (Cronbach's alpha 0.70-0.86). The procedure was feasible but only moderately acceptable to students. CONCLUSION: Our authentic assignment provided a valid, reliable and feasible procedure to assess our students. Acceptability was moderate, probably due to teething problems in instructions given and unfamiliarity with the format. Consequential validity data are lacking and would be of value. Our instrument could be an interesting alternative to other validated tests that may be less authentic.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".