Building for Success: Mental Health Research With an Integrated Knowledge Translation Approach
Bibliographic record
Abstract
Integrated knowledge translation approaches are one way to facilitate knowledge exchange and support the use of research in practice. This paper explores the elements of an integrated knowledge translation approach using the Systems Enhancement Evaluation Initiative (SEEI) as a case study. SEEI was a 4-year project (2005-2009) that explored the impacts of new funding in Ontario's community mental health system. Here, we describe the process, relationships, and challenges of this collaborative research initiative using a building analogy: getting the right people to do the work, designing the architectural blueprints, establishing the structure, and coordinating all facets of the project. We pay particular attention to the associated constraints and benefits when conducting a large-scale multisite evaluation.
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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 | 0.148 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.013 | 0.039 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".