Facilitating Knowledge Translation in the “Real World” of Community Psychiatry
Bibliographic record
Abstract
BACKGROUND: Tobacco use disproportionately affects the well-being of individuals with mental illness. In community psychiatric settings, there are culturally embedded attitudes and behaviors regarding smoking that enable practitioners to remain ambivalent about their clients' tobacco use. OBJECTIVES: Given these cultural norms, the authors aimed to introduce evidence-informed smoking cessation interventions to a variety of interdisciplinary mental health care providers by using an innovative approach to knowledge translation. DESIGN: The authors used a case study design in which six community psychiatric settings were targeted. The organizational culture related to smoking was examined at each site before tailored tobacco reduction interventions were delivered. The study design was guided by the knowledge-to-action (KTA) process and two supplementary approaches to change: motivational interviewing (MI) and appreciative inquiry (AI). RESULTS/CONCLUSIONS: The principles of the KTA process, MI, and AI helped the authors to meaningfully engage with practice groups and change the organizational culture surrounding tobacco use in several community psychiatric settings.
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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 | 0.054 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| 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".