Student perceptions of assessment and feedback in longitudinal integrated clerkships
Bibliographic record
Abstract
OBJECTIVES: This study was conducted to elucidate how the learning environment and the student-preceptor relationship influence student experiences of being assessed and receiving feedback on performance. Thus, we examined how long-term clinical clerkship placements influence students' experiences of and views about assessment and feedback. METHODS: We took a constructivist grounded approach, using authentic assessment and communities of practice as sensitising concepts. We recruited and interviewed 13 students studying in longitudinal integrated clerkships across two medical schools and six settings, using a semi-structured interview framework. We used an iterative coding process to code the data and arrive at a coding framework and themes. RESULTS: Students valued the unstructured assessment and informal feedback that arose from clinical supervision, and the sense of progress derived from their increasing responsibility for patients and acceptance into the health care community. Three themes emerged from the data. Firstly, students characterised their assessment and feedback as integrated, developmental and longitudinal. They reported authenticity in the monitoring and feedback that arose from the day-to-day delivery of patient care with their preceptors. Secondly, students described supportive and caring relationships and a sense of safety. These enabled them to reflect on their strengths and weaknesses and to interpret critical feedback as supportive. Students developed similar relationships across the health care team. Thirdly, the long-term placement provided for multiple indicators of progress for students. Patient outcomes were perceived as representing direct feedback about students' development as doctors. Taking increasing responsibility for patients over time is an indicator to students of their increasing competence and contributes to the developing of a doctor identity. CONCLUSIONS: Clerkship students studying for extended periods in one environment with one preceptor perceive assessment and feedback as authentic because they are embedded in daily patient care, useful because they are developmental and longitudinal, and constructive because they occur in the context of a supportive learning environment and relationship.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".