BETTER HEALTH: Durham -- protocol for a cluster randomized trial of BETTER in community and public health settings
Bibliographic record
Abstract
BACKGROUND: The Building on Existing Tools to Improve Chronic Disease Prevention and Screening (BETTER) cluster randomized trial in primary care settings demonstrated a 30% improvement in adherence to evidence-based Chronic Disease Prevention and Screening (CDPS) activities. CDPS activities included healthy activities, lifestyle modifications, and screening tests. We present a protocol for the adaptation of BETTER to a public health setting, and testing the adaptation in a cluster randomized trial (BETTER HEALTH: Durham) among low income neighbourhoods in Durham Region, Ontario (Canada). METHODS: The BETTER intervention consists of a personalized prevention visit between a participant and a prevention practitioner, which is focused on the participant's eligible CDPS activities, and uses Brief Action Planning, to empower the participant to set achievable short-term goals. BETTER HEALTH: Durham aims to establish that the BETTER intervention can be adapted and proven effective among 40-64 year old residents of low income areas when provided in the community by public health nurses trained as prevention practitioners. Focus groups and key informant interviews among stakeholders and eligible residents of low income areas will inform the adaptation, along with feedback from the trial's Community Advisory Committee. We have created a sampling frame of 16 clusters composed of census dissemination areas in the lowest urban quintile of median household income, and will sample 10 clusters to be randomly allocated to immediate intervention or six month wait list control. Accounting for the clustered design effect, the trial will have 80% power to detect an absolute 30% difference in the primary outcome, a composite score of completed eligible CDPS actions six months after enrollment. The prevention practitioner will attempt to link participants without a primary care provider (PCP) to a local PCP. The implementation of BETTER HEALTH: Durham will be evaluated by focus groups and key informant interviews. DISCUSSION: The effectiveness of BETTER HEALTH: Durham will be tested for delivery in low income neighbourhoods by a public health department. 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 | 0.057 | 0.065 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.169 | 0.023 |
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".