The Fountain of Health: Bringing Seniors’ Mental Health Promotion into Clinical Practice
Bibliographic record
Abstract
BACKGROUND: The Fountain of Health (FoH) initiative offers valuable evidence-based mental health knowledge and provides clinicians with evaluated tools for translating knowledge into practice, in order to reduce seniors' risks of mental disorders, including dementia. METHODS: A presentation on mental health promotion and educational materials were disseminated to mental health clinicians including physicians and other allied health professionals either in-person or via tele-education through a provincial seniors' mental health network. Measures included: 1) a tele-education quality evaluation form, 2) a knowledge transfer questionnaire, 3) a knowledge translation-to-practice evaluation tool, and 4) a quality assurance questionnaire. RESULTS: A total of 74 mental health clinicians received the FoH education session. There was a highly significant (p < .0001) difference in clinicians' knowledge transfer questionnaire scores pre- and post-educational session. At a two-month follow-up, 19 (25.7%) participants completed a quality assurance questionnaire, with all 19 (100%) of respondents stating they would positively recommend the FoH information to colleagues and patients. Eleven (20.4%) translation-to-practice forms were also collected at this interval, tracking clinician use of the educational materials. CONCLUSIONS: The use of a formalized network for knowledge transfer allows for education and evaluation of health-care practitioners in both acquisition of practical knowledge and subsequent clinical behavior change.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".