Youth Mental Health, Family Practice, and Knowledge Translation Video Games about Psychosis: Family Physicians' Perspectives.
Bibliographic record
Abstract
OBJECTIVE: Family practitioners face many challenges providing mental healthcare to youth. Digital technology may offer solutions, but the products often need to be adapted for primary care. This study reports on family physicians' perspectives on the relevance and feasibility of a digital knowledge translation (KT) tool, a set of video games, designed to raise awareness about psychosis, marijuana use, and facilitate access to mental health services among youth. METHOD: As part of an integrated knowledge translation project, five family physicians from a family health team participated in a focus group. The focus group delved into their perspectives on treating youth with mental health concerns while exploring their views on implementing the digital KT tool in their practice. Qualitative data was analyzed using thematic analysis to identify patterns, concepts, and themes in the transcripts. RESULTS: Three themes were identified: (a) challenges in assessing youth with mental health concerns related to training, time constraints, and navigating the system; (b) feedback on the KT tool; and, (c) ideas on how to integrate it into a primary care practice. CONCLUSIONS: Family practitioners felt that the proposed video game KT tool could be used to address youth's mental health and addictions issues in primary care 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".