Improving the Capacity of Non-Physician Primary Care Providers to Address Child and Youth Mental Health Through Mental Health Literacy Approaches
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Canadians rely on their primary care providers to address their mental health needs, but there are longstanding system gaps that must be addressed to enhance their ability to deliver mental health care. The present study addressed the gap in pediatric mental health care through the development, delivery, and evaluation of a mental health literacy training among non-physician primary care providers. METHODS: We delivered the training among 97 participants, with all completed the pre-test survey, and 74 completed the post-test survey on knowledge, attitudes toward mental health, and help-seeking intentions. Additionally, participants explained why they attended the training and shared how they would apply the knowledge learned into their practice (behaviors). RESULTS: = .274. However, participants' scores were exceedingly high on both outcomes, indicating positive attitudes and intentions at 2 time points and implying a ceiling effect of both outcomes. We did not find outcome differences by demographics, practice year, practice zone, or professional role. While knowledge, years of practice and prior mental health training predicted participants attitudes at pre-test, they didn't at post-test. Attitudes toward mental health predicted help-seeking intentions. Participants indicated this training will change their practice behaviors. CONCLUSION: This mental health literacy training for primary care providers demonstrated strong evidence of the need to integrate mental health and addiction support into primary care 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".