Storying My Lifestyle Change: How Breast Cancer Survivors Experience and Reflect on Their Participation in a Pilot Healthy Lifestyle Intervention
Bibliographic record
Abstract
Purpose: Healthy lifestyle interventions after breast cancer treatment have generally been studied in terms of weight-loss outcomes, which leaves a gap in our understanding of the phenomenological experience of such programs. Our knowledge of how or why women recovering from breast cancer engage or do not engage in these programs is limited. Thus, we aimed to share subjective experiences of lifestyle change within a 12-week group intervention entitled “Healthy Lifestyle Modification After Breast Cancer” (HLM-ABC).Methods: The present research entailed a multiple case study of four breast cancer survivors who participated in the HLM-ABC. Participants were interviewed longitudinally at four time-points: (1) pre-intervention; (2) mid-way intervention; (3) post-intervention, and (4) three-months post-intervention.Results: We analysed storytelling of participation in the HLM-ABC program to investigate participants’ unique and gradual endeavours towards living a healthier lifestyle. A qualitative, narrative analysis was applied to each participant’s set of interviews, which yielded two distinct story-telling patterns while participating in the HLM-ABC program: one “plot-driven” and one “character-driven”.Conclusions: These two narrative styles appeared to correspond with differing levels of intervention uptake and perceived success in the program. The implications of these narrative styles and their relationship to healthy lifestyle intervention are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".