Reflective writing and its impact on empathy in medical education: systematic review
Bibliographic record
Abstract
PURPOSE: Medical schools are increasingly aware of the ways in which physician empathy can have a profound impact on patients' lives and have developed humanities initiatives to address this concern. Reflective writing in particular is more commonly promoted in medical curricula, but there is limited research on the impact of reflective writing on medical student empathy levels. It aims to find the emotional effects of reflective writing interventions on medical and healthcare students by systemic review. METHODS: Two investigators independently reviewed educational publications for critical analysis. This review focused systematically on quantitative papers that measure the impact of reflective writing on empathy. RESULTS: Of the 1,032 studies found on Medline and CINAHL, only 8 used quantitative measures pre- and postwritten reflection to measure any impact on empathy outcomes. The outcomes measured included impact of reflective writing exercises on student wellness, aptitude, and/or clinical skills. Of these studies, a significant change in student empathy was observed in 100% of the studies, demonstrating a significant change in outcomes. CONCLUSION: Although the lack of homogeneity in outcome measurement in the literature limits possible conclusion from this review, the overwhelmingly positive reporting of outcomes suggests that reflective writing should be considered in any medical curriculum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".