Cocreating Meaning Through Expressive Writing and Reading for Cancer Caregivers
Bibliographic record
Abstract
PURPOSE: Caregivers of patients with cancer cope with socioemotional challenges, which can adversely affect their well-being. We developed an intervention, expressive writing and reading (EWR), to promote emotional processing and social connectedness among caregivers. In a single-arm pilot study, we assessed its feasibility and perceived usefulness. METHODS: Caregivers participated in weekly 1.5-hour EWR workshops offered over 20 weeks. After 4 sessions, they completed semistructured interviews, which were analyzed using qualitative descriptive analysis. FINDINGS: Of 65 caregivers approached, 25 were eligible, 18 consented, and 9 (50%) caregivers completed at least 4 workshops and the interview. Their responses revealed 3 themes: "inner processing," "interpersonal learning," and "enhanced processing and preparedness." Perceived benefits of EWR included emotional and cognitive processing (individual and collaborative), learning from the emotions and experiences of other caregivers, and preparing for upcoming challenges. CONCLUSIONS: Expressive writing and reading can be a safe and cost-effective supportive intervention for caregivers of patients with cancer.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".