Advancing prevention and screening in younger adults living with low income: development, piloting and acceptability/appropriateness evaluation of A BETTER Life
Bibliographic record
Abstract
BACKGROUND: In the original BETTER (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care) intervention, a "Prevention Practitioner" meets with a participant aged 40-65 years to improve their uptake of prevention activities (e.g. cancer screening, physical activity). The BETTER intervention was found to be effective in a randomised control trial. We adapted BETTER to focus on a younger age group (adults aged 18-39 years) living with low income, a group known to have a higher prevalence of preventable cancers and chronic diseases than their higher-income peers. Here, we describe the development, piloting, and qualitative evaluation of the acceptability of the adapted BETTER intervention ("BETTER Life") to inform future large-scale implementation research. METHODS: To support adaptation of BETTER, we interviewed community residents from low-income areas in Durham Region, Ontario, Canada and healthcare program service providers across Canada who had knowledge about preventive care. We developed an adapted intervention, BETTER Life, and piloted it at the Durham Community Health Centre to understand acceptability and appropriateness. Pilot participants were contacted a minimum of 2 weeks afterward to complete a semi-structured interview and share their experiences with the intervention and preventive care. RESULTS: We conducted 22 adaptation interviews with 10 community residents and 12 healthcare service providers, 6 interviews with pilot participants (of 8), and a focus group with the two Prevention Practitioners. We found that participants felt that poverty contributes to poor health, including mental health; health education and interventions are often missing, unknown, or difficult to access in low-income communities; and that social networks are important for health. As a direct response to these issues, BETTER Life was seen as a unique, comprehensive program in the community that helps people set goals and reinforce healthy behaviours. However, many different strategies may be required to encourage engagement in the BETTER Life program. CONCLUSIONS: We developed BETTER Life by adapting the original BETTER to focus on adults aged 18-39 years living with low income, piloted it, and evaluated its acceptability and appropriateness. Although BETTER Life was seen as an important program, recruitment for the larger-scale study will be challenging as young adults struggle with competing life priorities and the social determinants of health.
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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 | 0.005 | 0.003 |
| 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".