Integrated knowledge translation in mental health: family help as an example.
Bibliographic record
Abstract
OBJECTIVE: To describe and provide an example of integrated knowledge translation. METHODS: We review the elements of integrated knowledge translation and describe the Family Help Program, a distance treatment program for child mental health as an example of integrated knowledge translation. RESULTS: Family Help, a distance treatment program for child mental health, was developed with a grant from the Canadian Institutes of Health Research (CIHR). One of the requirements of the grant was involvement of community partners. This partnership resulted in a form of integrated knowledge translation (KT). To be successful, integrated KT requires the engagement of all partners and maintenance of mutual respect. The grant met its objectives and several distance treatments for child mental health were developed and evaluated. Integrated KT was effective in supporting the transfer of this research project into clinical practice and Family Help is now employed in several collaborating health districts. CONCLUSION: Integrated KT in the early phases of research has significant advantages when the purpose is inclusion of key stakeholders' (e.g. decision makers and consumers) knowledge to yield an effective product and facilitate uptake into clinical practice.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".