Feedback practices in clinical placement: how students come to understand how they are progressing
Bibliographic record
Abstract
Health clinicians are required to make use of feedback to form effective judgements and deliver efficacious health care. In health education, clinical placements provide an opportunity for students to translate knowledge to practise and develop expertise. During placements students are expected to engage with and act on feedback in order to improve and demonstrate competence. Increasingly, in the assessment literature, students’ ability to judge the quality of their own performance (known as evaluative judgement) is considered key to placement success. Despite this, there is limited empirical research examining student feedback practices and their influence on evaluative judgement in the placement environment. This study sought to examine feedback practices in clinical placement and their contribution to the development of evaluative judgement. Three participant groups, occupational therapy students, placement supervisors, and university placement support staff took part in semi-structured interviews. Data were analysed using document and thematic analyses. Findings suggest students use a range of active feedback practices during placement, seeking feedback from multiple sources in different forms. Feedback practices appear pivotal in developing evaluative judgement but this often requires patching together of disparate, complex and competing information. Placement supervisors need to scaffold feedback so that students can develop evaluative judgement.
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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 | 0.030 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".