Does direct observation influence the quality of workplace‐based assessment documentation?
Bibliographic record
Abstract
Background: A key component of competency-based medical education (CBME) is direct observation of trainees. Direct observation has been emphasized as integral to workplace-based assessment (WBA) yet previously identified challenges may limit its successful implementation. Given these challenges, it is imperative to fully understand the value of direct observation within a CBME program of assessment. Specifically, it is not known whether the quality of WBA documentation is influenced by observation type (direct or indirect). Methods: The objective of this study was to determine the influence of observation type (direct or indirect) on quality of entrustable professional activity (EPA) assessment documentation within a CBME program. EPA assessments were scored by four raters using the Quality of Assessment for Learning (QuAL) instrument, a previously published three-item quantitative measure of the quality of written comments associated with a single clinical performance score. An analysis of variance was performed to compare mean QuAL scores among the direct and indirect observation groups. The reliability of the QuAL instrument for EPA assessments was calculated using a generalizability analysis. Results: A total of 244 EPA assessments (122 direct observation, 122 indirect observation) were rated for quality using the QuAL instrument. No difference in mean QuAL score was identified between the direct and indirect observation groups (p = 0.17). The reliability of the QuAL instrument for EPA assessments was 0.84. Conclusions: Observation type (direct or indirect) did not influence the quality of EPA assessment documentation. This finding raises the question of how direct and indirect observation truly differ and the implications for meta-raters such as competence committees responsible for making judgments related to trainee promotion.
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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 | 0.032 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".