Capturing the Experience
Bibliographic record
Abstract
BACKGROUND: Expressive writing has been shown to improve quality of life, fatigue, and posttraumatic stress among breast cancer patients across cultures. Understanding how and why the method may be beneficial to patients can increase awareness of the psychosocial impact of breast cancer and enhance interventional work within this population. Qualitative research on experiential aspects of interventions may inform the theoretical understanding and generate hypotheses for future studies. AIM: The aim of the study was to explore and describe the experience and feasibility of expressive writing among women with breast cancer following mastectomy and immediate or delayed reconstructive surgery. METHODS: Seven participants enrolled to undertake 4 episodes of expressive writing at home, with semistructured interviews conducted afterward and analyzed using experiential thematic analysis. RESULTS: Three themes emerged through analysis: writing as process, writing as therapeutic, and writing as a means to help others. CONCLUSIONS: Findings illuminate experiential variations in expressive writing and how storytelling encourages a release of cognitive and emotional strains, surrendering these to reside in the text. The method was said to process feelings and capture experiences tied to a new and overwhelming illness situation, as impressions became expressions through writing. Expressive writing, therefore, is a valuable tool for healthcare providers to introduce into the plan of care for patients with breast cancer and potentially other cancer patient groups. IMPLICATIONS FOR PRACTICE: This study augments existing evidence to support the appropriateness of expressive writing as an intervention after a breast cancer diagnosis. Further studies should evaluate its feasibility at different time points in survivorship.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".