Strengthening Chronic Disease Prevention Programming: the Toward Evidence-Informed Practice (TEIP) Program Assessment Tool
Bibliographic record
Abstract
Best practices identified solely on the strength of research evidence may not be entirely relevant or practical for use in community-based public health and the practice of chronic disease prevention.Aiming to bridge the gap between best practices literature and local knowledge and expertise, the Ontario Public Health Association, through the Toward Evidence-Informed Practice initiative, developed a set of resources to strengthen evidence-informed decision making in chronic disease prevention programs.A Program Assessment Tool, described in this article, emphasizes better processes by incorporating review criteria into the program planning and implementation process.In a companion paper, "Strengthening Chronic Disease Prevention Programming: The Toward Evidence-Informed Practice (TEIP) Program Evidence Tool," we describe another tool, which emphasizes better evidence by providing guidelines and worksheets to identify, synthesize, and incorporate evidence from a range of sources (eg, peer-reviewed literature, gray literature, local expertise) to strengthen local programs.The Program Assessment Tool uses 19 criteria derived from literature on best and promising practices to assess and strengthen program planning and implementation.We describe the benefits, strengths, and challenges in implementing the tool in 22 community-based chronic disease prevention projects in Ontario, Canada.The Program Assessment Tool helps put best processes into operation to complement adoption and adaptation of evidenceinformed practices for chronic disease prevention.
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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 | 0.152 | 0.334 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".