Workplace-based assessment in the Bhutanese context: acceptability, feasibility, and educational impact as perceived by trainees and trainers
Bibliographic record
Abstract
Abstract Background:The Postgraduate Medical Education globally has transited from traditional cognitive based to more competency-based learning. Bhutan’s only medical university, Khesar Gyalpo University of Medical Sciences of Bhutan (KGUMSB) introduced Competency Based curriculum (CBC) through implementation of workplace-based assessment (WPBA) in June 2018. The proposed competency-based curriculum (CBC) was aimed at developing appropriate competencies in the learners through workplace-based assessment. A programmatic evaluation of the trainees and trainer’s perception on implementation of workplace-based assessment for three years at KGUMSB was conducted in July-Sept 2021. Methods: The evaluation was conducted in July-Sept, 2021. The mixed methods design was utilized such as survey, review of student portfolios and focus group discussion. A total of 62 participants (46 residents in clinical training and 16 faculty members) participated in this evaluation. Results: After three years of implementation of WPBA, it was perceived as a good system of assessing learners with a high level of acceptability among both the students and faculty members. The practice of providing immediate feedback was well appreciated by students. Conclusions: These findings support that WPBA is a good assessment system in postgraduate education. However, it was also evident that issues such as perceived time constraints, overburdened students and lack of faculty capacity were possible obstacles to proper implementation of WPBA.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".