A phenomenological study of resident and faculty experiences with learner engagement in the normalization of workplace-based assessment
Bibliographic record
Abstract
Background: Workplace-based assessments (WPBA) have become integral to learner-centred medical education. As previous research has linked learner engagement to WPBA implementation, this study explores residents' and faculty members' experiences with learner engagement in the normalisation of WPBA practice. Methods: Transcendental phenomenology was used as the qualitative approach, focusing on the participants' lived experiences. A semi-structured interview guide was used to interview five faculty members and five residents who had conducted WPBA. The interviews were transcribed and analysed using phenomenological data analysis. Results: Three themes were identified between learner engagement and WPBA conduct: (a) work environment, (b) roles and relationships, and (c) mutually beneficial teaching and learning. WPBA learner engagement occurred when participants interacted with each other and with the clinical setting to facilitate teaching and learning. Both participant groups reported a desire to participate in WPBA, but time constraints at times hindered participation. The residents indicated that WPBA improved their knowledge and admitted to experiencing negative emotions during the assessment. Overall, participants recognised the reciprocal benefits of WPBA participation for their professional development. Conclusion: The findings of the study suggest that learner engagement influences the use of WPBA. Consequently, it may be beneficial to consider the role of learner engagement to normalise WPBA application for teaching and learning in the clinical context.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".