Healthy minds/healthy children outreach service: lessons learned after eight years.
Bibliographic record
Abstract
OBJECTIVES: This article describes the Healthy Minds/Healthy Children Outreach Service (HMHC), an ongoing clinical and educational outreach service which makes use of technology to bridge geographical barriers to help build capacity in front-line professionals to meet children's mental health needs in rural areas. METHOD: A description of the HMHC clinical consultation and educational services is given. Utilization patterns of these services are reviewed. RESULTS: Clinical service accounts for approximately 1/3 of the service's activities. Continuing professional development has experienced strong growth since the program's inception eight years ago. The majority of consultees and continuing professional development users have been non-physicians. DISCUSSION: Future challenges for program development include increasing physician involvement and continuing to adapt the program's continuing education program to the multidisciplinary professionals who provide support to children in rural areas. Measuring the program's outcome in terms of its effect on clinical care through knowledge transfer has been difficult to do because of methodological research challenges, while successful research in this area will be helpful to determine how collaborative care models can help in the provision of mental health services to youth in rural communities. The growth of collaboration across various professional disciplines and service sectors demonstrates that programs like HMHC can be effective in meeting some of the unmet needs in providing mental health services to children and youth.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".