W13– Primary prevention in primary care
Bibliographic record
Abstract
Guideline implementation Incorporating guidelines into health care systems The BETTER (Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice) project aims to improve chronic disease prevention and screening for heart disease, diabetes, and cancer, including lifestyle factors. This workshop will describe the process by which information across disease areas was selected and integrated into implementation strategies at both practice and patient levels (e.g., audit and feedback, computer-based decision support and prompts, prevention practitioners, etc.) The emphasis was to build from what already exists and to leverage the knowledge of the participating practices to develop effective toolkits and strategies for implementation. The first step in this process was to review current guideline recommendations and existing tools to determine relevance to the primary care setting and feasibility for uptake. Guidelines published in each clinical area were identified using a very direct search strategy that focused on currency and relevance to the clinical setting. Guidelines were then evaluated using the AGREE domains as a guide in order to determine which had good rigor of development, editorial independence, and had recommendations that were linked directly to the evidence. Recommendations from the top three to five highest ranking guidelines were extracted along with their levels of evidence. These recommendations were compared to one another and considered within the local context to select the recommendations that would form the basis for our interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".