Building on existing tools to improve chronic disease prevention and screening in public health: a cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: The BETTER (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care) intervention was designed to integrate the approach to chronic disease prevention and screening in primary care and demonstrated effective in a previous randomized trial. METHODS: We tested the effectiveness of the BETTER HEALTH intervention, a public health adaptation of BETTER, at improving participation in chronic disease prevention and screening actions for residents of low-income neighbourhoods in a cluster randomized trial, with ten low-income neighbourhoods in Durham Region Ontario randomized to immediate intervention vs. wait-list. The unit of analysis was the individual, and eligible participants were adults age 40-64 years residing in the neighbourhoods. Public health nurses trained as "prevention practitioners" held one prevention-focused visit with each participant. They provided participants with a tailored prevention prescription and supported them to set health-related goals. The primary outcome was a composite index: the number of evidence-based actions achieved at six months as a proportion of those for which participants were eligible at baseline. RESULTS: Of 126 participants (60 in immediate arm; 66 in wait-list arm), 125 were included in analyses (1 participant withdrew consent). In both arms, participants were eligible for a mean of 8.6 actions at baseline. At follow-up, participants in the immediate intervention arm met 64.5% of actions for which they were eligible versus 42.1% in the wait-list arm (rate ratio 1.53 [95% confidence interval 1.22-1.84]). CONCLUSION: Public health nurses using the BETTER HEALTH intervention led to a higher proportion of identified evidence-based prevention and screening actions achieved at six months for people living with socioeconomic disadvantage. TRIAL REGISTRATION: NCT03052959 , registered February 10, 2017.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".