Students’ satisfaction with general practitioners’ feedback to their reflective writing: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Reflective Writing (RW) is increasingly being implemented in medical education. Feedback to students' reflective writing (RW) is essential, but resources for individualized feedback often lack. We aimed to determine whether general practitioners (GPs) teaching students clinical skills could also provide feedback to RW and whether an instruction letter specific to RW feedback increases students' satisfaction. METHODS: GPs were randomized to the two study arms using block randomization. GPs in both groups received an instruction letter on giving students feedback on clinical skills. Additionally, intervention group GPs received specific instructions on providing feedback to students' RW. Students completed satisfaction questionnaires on feedback received on clinical skills and RW. T-tests were employed for all statistical analysis to compare groups. RESULTS: Eighty-three out of 134 physicians participated: 38 were randomized to the control, 45 to the intervention group. Students were very satisfied with the feedback on RW and clinical skills regardless of tutors' group allocation. A specific instruction letter had no additional effect on students' satisfaction. CONCLUSION: Based on student satisfaction, GPs who give students feedback on clinical skills are also well suited to provide feedback on RW. This approach can facilitate the introduction of mandatory RW into the regular medical curriculum.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".