Mental health and addictions capacity building for community health centres in Ontario.
Bibliographic record
Abstract
PROBLEM ADDRESSED: In recent years, there has been increased recognition in Canada of the need to strengthen mental health services in primary health care (PHC). Collaborative models, including partnerships between PHC and specialized mental health care providers, have emerged as effective ways for improving access to mental health care and strengthening clinical capacity. Primary health care physicians and other health professionals are well positioned to facilitate the early detection of mental disorders and provide appropriate treatment and follow-up care, helping to tackle stigma toward mental health problems in the process. OBJECTIVE OF PROGRAM: This 4-year mental health and addiction capacity-building initiative for PHC addressed competency needs at the individual, interprofessional, and organizational levels. PROGRAM DESCRIPTION: The program included 5 key components: a needs assessment; interprofessional education; mentoring; development of organizational mental health and addiction action plans for each participating community health centre; and creation of an advanced resource manual to support holistic and culturally competent collaborative mental health care. A comprehensive evaluation framework using a mixed-methods approach was applied from the initiation of the program. A total of 184 health workers in 10 community health centres in Ontario participated in the program, including physicians, nurses, social workers, and administrative staff. CONCLUSION: Evaluation findings demonstrated high satisfaction with the training, improved competencies, and individual behavioural and organizational changes. By building capacity to integrate holistic and culturally appropriate care, this competency-based program is a promising model with strong potential to be adapted and scaled up for PHC organizations nationally and internationally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".