Health Behavior Change Intervention Preferences Expressed by American Indian Cancer Survivors From a Southwest Tribal Community: Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: While many factors, including social determinants of health, affect cancer mortality, one modifiable risk factor that may contribute to cancer disparities is obesity. The prevalence of obesity in the American Indian/Alaska Native population is 48.1% per the Centers for Disease Control and Prevention. The overall cancer mortality for the American Indian/Alaska Native population is 18% higher than the White population as reported by the American Cancer Society. Interventions tailored to American Indian/Alaska Native communities that promote healthy lifestyle behaviors after cancer diagnosis and prior to cancer surgery (prehab) might improve cancer outcomes for this population. OBJECTIVE: The aim of the study is to characterize the lifestyle behaviors of San Carlos Apache cancer survivors and identify preferences for the adaption of a prehab intervention. METHODS: Semistructured interviews and validated questionnaires were completed with San Carlos Apache cancer survivors (N=4), exploring their viewpoints on healthy lifestyle and cancer risk and preferences for program development. A thematic content analysis was conducted. RESULTS: and walked 53 minutes daily. The majority of participants reported a high willingness to change eating habits (n=3, 75%). All 4 reported willingness to participate in a diet and exercise program. Important themes and subthemes were identified: (1) cancer is perceived as a serious health condition in the community (N=4, 100%); (2) environmental exposures are perceived as cancer-causing threats (n=3, 75%); (3) healthy diet, exercise, and avoiding harmful substances are perceived as mitigating cancer risk (n=3, 75%); (4) barriers to healthy habits include distance to affordable groceries (n=3, 75%) and lack of transportation (n=2, 50%); (5) there is high interest in a prehab program geared toward patients with cancer (N=4, 100%); and (6) standard monitoring practiced in published prehab programs showed early acceptability with participants (N=4, 100%). CONCLUSIONS: Collaboration with tribal partners provided important insight that can help inform the adaptation of a culturally appropriate prehab program for San Carlos Apache patients diagnosed 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".