Reflections from Key Policy Decision-makers on Integrated Care and the Value of Decision-maker Involvement in Research
Bibliographic record
Abstract
The iCOACH study involved key health care system decision-makers from Ontario, Quebec and New Zealand. This article is written by the key decision-makers involved in the iCOACH study and discusses their motivations to engage in the research project, the value of participation and key recommendations for best practices to engage decision-makers in research projects. Suggestions for knowledge translation are identified including practical tools for decision-makers and providers to use to assess readiness to implement integrated community-based primary health care. Case study briefs with key enablers and 'talking-points' and infographics are similarly recommended as approaches to transfer knowledge gained from this research study.
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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 | 0.430 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.066 |
| Scholarly communication | 0.054 | 0.026 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.029 | 0.068 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".