Implementing stakeholder-informed research in the substance abuse treatment sector: strategies used by Connections, a Canadian knowledge translation and exchange project
Bibliographic record
Abstract
BACKGROUND: Researcher-stakeholder collaboration has been identified as critical to bridging research and health system change. While collaboration models vary, meaningful stakeholder involvement over time ("integrated knowledge translation") is advocated to improve the relevance of research to knowledge users. This short report describes the integrated knowledge translation efforts of Connections, a knowledge translation and exchange project to improve services for women with substance abuse problems and their children, and implementation barriers and facilitators. FINDINGS: Strategies of varying intensities were used to engage diverse stakeholders, including policy makers and people with lived experience, and executive directors, program managers, and service providers from Canadian addiction agencies serving women. Barriers to participation included individual (e.g., interest), organizational (e.g., funding), and system level (e.g., lack of centralized stakeholder database) barriers. Similarly, facilitators included individual (e.g., perceived relevance) and organizational (e.g., support) facilitators, as well as initiative characteristics (e.g., multiple involvement opportunities). Despite barriers, Connections' stakeholder-informed research efforts proved essential for developing clinically relevant and feasible processes, measures, and implementation strategies. CONCLUSIONS: Stakeholder-researcher collaboration is possible and robust integrated knowledge translation efforts can be productive. Future work should emphasize developing and evaluating a range of strategies to address stakeholders' knowledge translation needs and to facilitate sustained and meaningful involvement in research.
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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 | 0.093 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.041 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".