Developing clinical decision tools to implement chronic disease prevention and screening in primary care: the BETTER 2 program (building on existing tools to improve chronic disease prevention and screening in primary care)
Bibliographic record
Abstract
BACKGROUND: The Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial demonstrated the effectiveness of an approach to chronic disease prevention and screening (CDPS) through a new skilled role of a 'prevention practitioner'(PP). The PP has appointments with patients 40-65 years of age that focus on primary prevention activities and screening of cancer (breast, colorectal, cervical), diabetes and cardiovascular disease and associated lifestyle factors. There are numerous and occasionally conflicting evidence-based guidelines for CDPS, and the majority of these guidelines are focused on specific diseases or conditions; however, primary care providers often attend to patients with multiple conditions. To ensure that high-level evidence guidelines were used, existing clinical practice guidelines and tools were reviewed and integrated into blended BETTER tool kits. Building on the results of the BETTER trial, the BETTER tools were updated for implementation of the BETTER 2 program into participating urban, rural and remote communities across Canada. METHODS: A clinical working group consisting of PPs, clinicians and researchers with support from the Centre for Effective Practice reviewed the literature to update, revise and adapt the integrated evidence algorithms and tool kits used in the BETTER trial. These resources are nuanced, based on individual patient risk, values and preferences and are designed to facilitate decision-making between providers across the target diseases and lifestyle factors included in the BETTER 2 program. Using the updated BETTER 2 toolkit, clinicians 1) determine which CDPS actions patients are eligible to receive and 2) develop individualized 'prevention prescriptions' with patients through shared decision-making and motivational interviewing. RESULTS: The tools identify the patients' risks and eligible primary CDPS activities: the patient survey captures the patient's health history; the prevention visit form and integrated CDPS care map identify eligible CDPS activities and facilitate decisions when certain conditions are met; and the 'bubble diagram' and 'prevention prescription' promote shared decision-making. CONCLUSION: The integrated clinical decision-making tools of BETTER 2 provide resources for clinicians and policymakers that address patients' complex care needs beyond single disease approaches and can be adapted to facilitate CDPS in the urban, rural and remote clinical setting. TRIAL REGISTRATION: The registration number of the original RCT BETTER trial was ISRCTN07170460 .
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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 | 0.125 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".