Improving Research Transfer in the Addictions Field: A Perspective from Canada
Bibliographic record
Abstract
This paper discusses how to enhance the research-to-practice exchange in the addictions field, while maintaining a balance with the demands and complexities of program delivery and policy development. It outlines the evolution of the concept of evidence-based practice, discusses the practical limitations and ways to improve transferring research to practice, and provides examples of research transfer activities in Canada. Practical limitations to research transfer include individual, organizational, and community factors. A strategic approach to research transfer includes addressing these limitations by combining dissemination activities with interventions such as individual instruction and incentives; building relationships among researchers, practitioners, and populations served; and obtaining commitments at a systemic level from funding bodies and research organizations to support research transfer. The potential is noted for the concept of workforce development to facilitate research transfer at organizational levels. The conclusion shown in this paper is that the tools and concept of evidence-based practice can lead the way to strengthening addictions programs and policies, and the development of a conceptual model for addiction research transfer in Canada would be a useful next step.
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.
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 | 0.088 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.035 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".