Assessing the effectiveness of “BETTER Women”, a community-based, primary care-linked peer health coaching programme for chronic disease prevention: protocol for a pragmatic, wait-list controlled, type 1 hybrid effectiveness-implementation trial
Bibliographic record
Abstract
INTRODUCTION: The Building on Existing Tools to Improve Cancer and Chronic Disease Prevention and Screening in Primary Care (BETTER) programme trains allied health professionals working in primary care settings to develop personalised chronic disease 'prevention prescriptions' with patients. However, maintenance of health behaviour changes is difficult without ongoing support. Sustainable options to enhance the BETTER programme and ensure accessibility to underserved populations are needed. We designed the BETTER Women programme, which uses a digital app to match patients with a trained peer health coach (PHC) who provides ongoing support for health behaviour change after receipt of a BETTER prevention prescription in primary care. METHODS AND ANALYSIS: We will conduct a type 1 hybrid implementation-effectiveness patient-randomised trial. Interested women aged 40-68 years will be recruited from three large, sociodemographically distinct primary care clinics (urban, suburban and rural). Patients will be randomised 1:1 to intervention or wait-list control after receipt of their BETTER prevention prescription. We will aim to recruit 204 patients per group (408 total). Effectiveness will be assessed by the primary outcome of targeted behaviours achieved for each participant at 6 months, consisting of three cancer screening tests (cervical, breast and colorectal) and four behavioural determinants of cancer and chronic disease (diet, smoking, alcohol use and physical activity). Data will be collected through patient survey and clinical chart review, measured at 3, 6 and 12 months. Implementation outcomes will be assessed through patient surveys and interviews with patients, peer health coaches and healthcare providers. An embedded economic evaluation will examine cost per quality-adjusted life-year and per additional health behavioural targets achieved. ETHICS AND DISSEMINATION: This study has been approved by Women's College Hospital Research Ethics Board (REB), the Royal Victoria Regional Health Centre REB and the University of Toronto REB. All participants will provide informed consent prior to enrolment. Participation is voluntary and withdrawal will have no impact on the usual care received from their primary care provider. The results of this trial will be published in peer-reviewed journals and shared via conference presentations. Deidentified datasets will be shared on request, after publication of results. TRIAL REGISTRATION NUMBER: NCT04746859.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".