The effectiveness of a proven chronic disease prevention and screening intervention in diverse and remote primary care settings: an implementation study on the BETTER 2 Program
Bibliographic record
Abstract
BACKGROUND: onic Disease Prevention and Screening in Primary Care (BETTER) randomised control trial (RCT) showed that the BETTER Program improved chronic disease prevention and screening (CDPS) by 32.5% in urban team-based primary care clinics. AIM: To evaluate outcomes from implementation of BETTER in diverse clinical settings. DESIGN & SETTING: An implementation study was undertaken to apply the CDPS intervention from the BETTER trial to diverse settings in BETTER 2. Patients aged 40-65 years were invited to enrol in the study from three clinics in Newfoundland and Labrador, Canada. METHOD: At baseline, eligibility for 27 CDPS actions (for example, cancer, diabetes and hypertension screening, lifestyle) was determined. Patients then met with a trained provider and prioritised goals to address their eligible CDPS actions. Providers received training in behaviour change theory and practice. Descriptive analysis of clinical outcomes and success factors were reported. RESULTS: A total of 154 patients (119 female and 35 male) had a baseline visit; 106 had complete outcome assessments, and the remainder had partial outcome assessments. At baseline, patients were eligible for a mean of 12.3 CDPS actions and achieved a mean of 6.0 (49%, 95% confidence intervals [CI] = 24% to 74%) at 6-month follow-up, including reduced hypertension (86% of eligible patients, 95% CI = 67% to 96%), weight control (51% of eligible patients, 95% CI = 42% to 60%), and smoking cessation (36% of eligible patients, 95% CI = 17% to 59%). Male, highly educated, and lower income individuals achieved a higher proportion of CDPS manoeuvers than their counterparts. CONCLUSION: Clinical outcomes from this implementation study were comparable with those of the prior BETTER RCT, providing support for the BETTER Program as an effective approach to CDPS in more diverse general practice settings.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".