A Narrative Review of Recent Developments in Knowledge Translation and Implications for Mental Health Care Providers
Bibliographic record
Abstract
OBJECTIVE: Attention to knowledge translation (KT) has increased in the health care field in an effort to improve uptake and implementation of potentially beneficial knowledge. We provide an overview of the current state of KT literature and discuss the relevance of KT for health care professionals working in mental health. METHOD: A systematic search was conducted using MEDLINE, PsycINFO, and CINAHL databases to identify review articles published in journals from 2007 to 2012. We selected articles on the basis of eligibility criteria and then added further articles deemed pertinent to the focus of ourpaper. RESULTS: After removing duplicates, we scanned 214 review articles for relevance and, subsequently, we added 46 articles identified through hand searches of reference lists or from other sources. A total of 61 papers were retained for full review. Qualitative synthesis identified 5 main themes: defining KT and development of KT science; effective KT strategies; factors influencing the effectiveness of KT; KT frameworks and guides; and relevance of KT to health care providers. CONCLUSIONS: Despite limitations in existing evidence, the concept and practice of KT holds potential value for mental health care providers. Understanding of, and familiarity with, effective approaches to KT holds the potential to enhance providers' treatment approaches and to promote the use of new knowledge in practice to enhance outcomes.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".