Integrated Knowledge Translation: Illustrated with Outcome Research in Mental Health
Bibliographic record
Abstract
Through this article the authors present a case summary of the early phases of research conducted with an Integrated Knowledge Translation (iKT) approach utilizing four factors: research question, research approach, feasibility, and outcome. iKT refers to an approach for conducting research in which community partners, referred to as knowledge users, are engaged in the entire research process. In this collaborative approach, knowledge users and researchers jointly devise the entire research agenda beginning with the development of the research question(s), determination of a feasible research design and feasible methods, interpretation of the results, dissemination of the findings, and the translation of knowledge into practice or policy decisions. Engaging clinical or community partners in the research enterprise can enhance the utility of the research results and facilitate its uptake. This collaboration can be a complex arrangement and flexibility may be required to accommodate the various configurations that the collaboration can take. For example, the research question can be jointly determined and refined; however, one person must take the responsibility for orchestrating the project, including preparing the proposal and application to the Research Ethics Board. This collaborative effort also requires the simultaneous navigation of barriers and facilitators to the research enterprise. Navigating these elements becomes part of the conduct of research with the potential for rewarding results, including an enriched work experience for clinical partners and investigators. One practice implication is that iKT may be considered of great utility to service providers due to its field friendly nature.
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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 | 0.149 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".