Strengthening Chronic Disease Prevention Programming: the Toward Evidence-Informed Practice (TEIP) Program Assessment Tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it