Exploring the Impact of Book Club Participation on Clinicians’ Empathy and Reflection on Empathetic Practice: A Wake-Up Call
Bibliographic record
Abstract
INTRODUCTION: Empathy is associated with desirable outcomes in healthcare, including improved patient-clinician rapport, fewer patient complications, and reduced clinician burnout. Despite these benefits, research suggests empathy declines during professional training. This study aimed to explore the impact of book club participation on clinicians' and trainees' empathy and perspectives on empathetic patient care. METHODS: In this mixed-methods study, anesthesiology clinicians and trainees were invited to respond to a baseline online empathy survey followed by an invitation to read a book and to participate in one of four facilitated book club sessions. Post-intervention empathy was measured. The primary outcome of the quantitative analysis was a change in empathy scores as measured by the Toronto Empathy Questionnaire. A thematic analysis of book club sessions and open-ended comments in the post-intervention survey was conducted. RESULTS: = 0.42, p=0.66). Thematic analysis of the book club sessions revealed four themes that highlight how the book club enhanced empathy awareness among trainees and clinicians: 1) a wake-up call, 2) deciding whether to take action, 3) learning and nurturing empathy, and 4) changing the culture. CONCLUSION: There were no significant changes in empathy scores associated with book club participation. Thematic analysis highlighted barriers toward empathetic patient care, areas for improvement, and voiced intentions to practice with heightened empathy. Book clubs may be a viable venue to nurture a culture of increased self-awareness and motivation to counteract loss of empathy, but just one experience may not be sufficient.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".