Investigating the effects of a short narratology module on empathy and patient-centred communication in early-stage medical students: an empirical mixed-methods pilot study
Bibliographic record
Abstract
BACKGROUND: Effective communication is a fundamental skill in clinical medicine; however, traditional approaches often fail to equip learners with an ability to authentically and empathically engage with the complexities of real patients' experiences. Narratology has been proposed as a pedagogical framework for augmenting empathy and patient-centred communication in medical students. METHODS: In April 2025, we undertook a mixed-methods pilot study to evaluate the impact of a one-week narratology module on second-year undergraduate medical students at the Royal College of Surgeons in Ireland (RCSI), Dublin. The module involved close reading, group viewings, facilitated small-group workshops, and whole-group discussions in response to narrative works by Irish writers and storytellers, followed by written personal reflections. At the outset and conclusion of the module, each student undertook a clinical history with a simulated patient (SP) portraying early-stage dementia. SPs assessed each student's empathy and communication using the Consultation and Relational Empathy (CARE) Measure. RESULTS: Overall, 30 medical students (age 20 ± 1.1 yrs, 59.1% female) were included; of these, 22 completed both SP encounters. Total CARE Measure score significantly increased post-intervention: median within-subject difference = 3.00 (-0.25, 10.00) [P = 0.0035]. Analysis of individual CARE Measure items revealed significant improvement post-intervention in "Q2: Letting you tell your story" (P = 0.0131), "Q3: Really listening" (P = 0.0474), "Q4: Being interested in you as a whole person" (P = 0.0474), "Q5: Fully understanding your concerns" (P = 0.0369), and "Q6: Showing care and compassion" (P = 0.0054). Qualitative analysis of students' written reflections (n = 30) identified three themes: (i) developing a safe and respectful communication environment; (ii) recognising the patient as a whole individual; and, (iii) growing in empathy and emotional connection. CONCLUSION: Our results show that empathy and communication in early-stage medical students can improve following a short narratology module. Further prospective studies are now required to explore the longer-term effects of narratology on patient-centred healthcare. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT07084077 (Retrospective registration).
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".