The challenges of detecting progress in generic competencies in the clinical setting
Bibliographic record
Abstract
Context Competency‐based medical education has spurred the implementation of longitudinal workplace‐based assessment (WBA) programmes to track learners’ development of competencies. These hinge on the appropriate use of assessment instruments by assessors. This study aimed to validate our assessment programme and specifically to explore whether assessors’ beliefs and behaviours rendered the detection of progress possible. Methods We implemented a longitudinal WBA programme in the third year of a primarily rotation‐based clerkship. The programme used the professionalism mini‐evaluation exercise (P‐ MEX ) to detect progress in generic competencies. We used mixed methods: a retrospective psychometric examination of student assessment data in one academic year, and a prospective focus group and interview study of assessors’ beliefs and reported behaviours related to the assessment. Results We analysed 1662 assessment forms for 186 students. We conducted interviews and focus groups with 21 assessors from different professions and disciplines. Scores were excellent from the outset (3.5–3.7/4), with no meaningful increase across blocks (average overall scores: 3.6 in block 1 versus 3.7 in blocks 2 and 3; F = 8.310, d.f. 2, p < 0.001). The main source of variance was the forms (47%) and only 1% of variance was attributable to students, which led to low generalisability across forms ( E ρ 2 = 0.18). Assessors reported using multiple observations to produce their assessments and were reluctant to harm students by consigning anything negative to writing. They justified the use of a consistent benchmark across time by citing the basic nature of the form or a belief that the ‘competencies’ assessed were in fact fixed attributes that were unlikely to change. Conclusions Assessors may purposefully deviate from instructions in order to meet their ethical standards of good assessment. Furthermore, generic competencies may be viewed as intrinsic and fixed rather than as learnable. Implementing a longitudinal WBA programme is complex and requires careful consideration of assessors’ beliefs and values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".