How progress evaluations are used in postgraduate education with longitudinal supervisor-trainee relationships: a mixed method study
Bibliographic record
Abstract
The combination of measuring performance and giving feedback creates tension between formative and summative purposes of progress evaluations and can be challenging for supervisors. There are conflicting perspectives and evidence on the effects supervisor-trainee relationships have on assessing performance. The aim of this study was to learn how progress evaluations are used in postgraduate education with longitudinal supervisor-trainee relationships. Progress evaluations in a two-year community-pharmacy specialization program were studied with a mixed-method approach. An adapted version of the Canadian Medical Education Directives for Specialists (CanMEDS) framework was used. Validity of the performance evaluation scores of 342 trainees was analyzed using repeated measures ANOVA. Semi-structured interviews were held with fifteen supervisors to investigate their response processes, the utility of the progress evaluations, and the influence of supervisor-trainee relationships. Time and CanMEDS roles affected the three-monthly progress evaluation scores. Interviews revealed that supervisors varied in their response processes. They were more committed to stimulating development than to scoring actual performance. Progress evaluations were utilized to discuss and give feedback on trainee development and to add structure to the learning process. A positive supervisor-trainee relationship was seen as the foundation for feedback and supervisors preferred the roles of educator, mentor, and coach over the role of assessor. We found that progress evaluations are a good method for directing feedback in longitudinal supervisor-trainee relationships. The reliability of scoring performance was low. We recommend progress evaluations to be independent of formal assessments in order to minimize roles-conflicts of supervisors.
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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 | 0.096 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".