Improving Primary Prevention and Screening: Knowledge Synthesis and Actionable Recommendations for the Better Program
Bibliographic record
Abstract
Context: Cancer and chronic disease prevention and screening (CCDPS) guidelines are not consistently applied in primary care. Furthermore, while most patients have multiple risks and conditions, guidelines are focused on a specific disease or organ system. The BETTER program (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Primary Care) involves an evidence-based intervention provided by an allied health professional within a primary care practice who acquires advanced skills in CCDPS and takes on the role of Prevention Practitioner (PP). Using the BETTER toolkit, created through a rigorous process of knowledge synthesis and harmonization of recommendations, the PP meets with patients for a personalized prevention visit. Objective: To describe the: 1) evidence review process used to identify high-quality clinical practice guidelines (CPGs), 2) harmonization of primary prevention and screening recommendations, and 3) identification, development, and refinement of resources and tools for inclusion in the BETTER toolkit. The BETTER toolkit will be used to inform CCDPS in rural, remote, and urban primary care settings across Canada. Methods: In 2017, the BETTER Program conducted a literature review of evidence-based CPGs published between 2010 and 2016. For this update, high-quality international, Canadian, and Provincial CPGs published between 2016 and 2021, focusing on primary prevention and screening of cancer and chronic disease, and applicable to patients 40-69 years of age were identified. A Clinical Working Group consisting of decision-makers, researchers, clinicians, and a patient representative across Canada was split up into 3 topic review teams. A total of 19 CCDPS topics within scope for BETTER were identified. Topic teams reviewed the literature and synthesized guidelines based on evidence for their topic and updated the toolkits to inform the PP role. Results: Development of an updated care map that considers family history and risk factor assessment that is tailored to the patient and adaptable to diverse practice settings. Conclusions: Synthesized and evidence-based integrated care plans can be used to assess patients’ CCDPS risk and preferences in diverse populations in Canada. The updated toolkit will facilitate the application of recommendations for the primary prevention of cancer and chronic disease in patients 40-69 years of age.
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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 | 0.254 | 0.426 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 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".