A national survey of organizational transfer practices in chronic disease prevention in Canada
Bibliographic record
Abstract
Underuse of best practices in chronic disease prevention (CDP) represents missed opportunities to promote healthy living and prevent chronic disease. Better understanding of how CDP programs, practices and policies (PPPs) are transferred from 'resource' organizations that develop them to 'user' organizations that implement them is crucial. The objectives of this work were to develop psychometrically sound measures of transfer practices occurring within resource organizations; describe the use of these transfer practices and identify correlates of the transfer process. Cross-sectional data were collected in structured telephone interviews with the person most knowledgeable about PPP transfer in 77 Canadian organizations that develop PPPs. Independent correlates of transfer were identified using multiple linear regression. The transfer practices most commonly used included: identification of barriers to PPP adoption/implementation, tailoring transfer strategies and designing a transfer plan. Skill at planning/implementing transfer, external sources of funding specifically allocated for transfer, type of resource organization, attitude toward process of collaboration and user-centeredness were all positively associated with the transfer process. These factors represent possible targets for interventions to improve transfer of CDP PPPs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".