Listening to Cancer Patients’ Narratives During Residency: A Pilot Study on a Communication Skills’ Workshop Involving Patients-Partners
Bibliographic record
Abstract
The field of cancer care still lacks best practices in communication. Few postgraduate training programs offer formal training to develop such skills. The patient partnership has been used in medical education to increase the sensitivity of the subjective experiences of patients. In our Canadian center, residents and patient-partners participated in an educational workshop on communication focusing on patient's narrative. The aim of this pilot qualitative study was to explore the experiences of participants in the workshop. Using theoretical sampling, we recruited 6 residents and 6 patient-partners. Semi-structured interviews were conducted and transcribed. A thematic analysis was performed. From analysis, 4 themes emerged: (1) lack of communication skills training; (2) barriers to effective communication in cancer care; (3) the empathy of patient-partners towards the communication challenges faced by residents; and (4) the participants' reactions to the workshop. Based on our findings, our communication skills workshop centered on narrative medicine and involving patient-partners appears feasible. Future research could study its pedagogical value and the optimal learning environment required.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".