Stories at Work: Writing to Learn, Care, and Collaborate in Radiation Therapy
Bibliographic record
Abstract
Narrative writing has shown potential to foster skilled, compassionate care among health professionals. We describe the process and effects of a project that introduced experiential narrative writing to professionals and students at a large Canadian cancer centre. Four 90-minute introductory workshops in experiential narrative writing were offered to radiation therapy students (9), radiation therapists (28), and oncology nurses (1). These workshops were followed by an in-depth narrative writing course consisting of four 60-minute sessions. The course was offered twice with a total of 11 participants (all radiation therapists). Participants were prompted to write about their experiences, share their writing, and respond to each other’s writing. Writing was not focused on professional experiences. All sessions were led by an experienced facilitator and researcher. In order to describe the process and effects of these courses, we used a combination of observations, reflective writing, ongoing dialogue with participants, and follow-up interviews (8 radiation therapists and 3 students). We describe five “active elements” of the narrative writing sessions: stories at (but not about) work, challenge, trust, quality of engagement, and continuity. We then discuss perceived effects of the narrative writing sessions, which we have termed pleasure, perspective, community, presence, craft, and collective artwork. These findings suggest potential for narrative writing to support the work, well-being, and community of health professionals in radiation therapy.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".