Health Promotion Among Mexican-Origin Survivors of Breast Cancer and Caregivers Living in the United States–Mexico Border Region: Qualitative Analysis From the Vida Plena Study
Bibliographic record
Abstract
BACKGROUND: Hispanic survivors of cancer experience increased cancer burden. Lifestyle behaviors, including diet and physical activity, may reduce the cancer burden. There is limited knowledge about the posttreatment lifestyle experiences of Hispanic survivors of cancer living on the United States-Mexico border. OBJECTIVE: This study aims to support the development of a stakeholder-informed, culturally relevant, evidence-based lifestyle intervention for Mexican-origin Hispanic survivors of cancer living in a border community to improve their dietary quality and physical activity. METHODS: Semistructured interviews with 12 Mexican-origin Hispanic survivors of breast cancer and 7 caregivers were conducted through internet-based teleconferencing. The interviews explored the impact of cancer on lifestyle and treatment-related symptoms, perception of lifestyle as an influence on health after cancer, and intervention content and delivery preferences. Interviews were analyzed using a deductive thematic approach grounded in the Quality of Cancer Survivorship Care Framework. RESULTS: Key survivor themes included perception of Mexican diet as unhealthy, need for reliable diet-related information, perceived benefits of physical activity after cancer treatment, family support for healthy lifestyles (physical and emotional), presence of cancer-related symptoms interfering with lifestyle, and financial barriers to living a healthy lifestyle. Among caregivers, key themes included effects of the cancer caregiving experience on caregivers' lifestyle and cancer-preventive behaviors and gratification in providing support to the survivors. CONCLUSIONS: The interviews revealed key considerations to the adaptation, development, and implementation of a theory-informed, evidence-based, culturally relevant lifestyle program to support lifestyle behavior change among Mexican-origin Hispanic survivors of cancer living in border communities. Our qualitative findings highlight specific strategies that can be implemented in health promotion programming aimed at encouraging cancer protective behaviors to reduce the burden of cancer and comorbidities in Mexican-origin survivors of cancer living in border communities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".