P01-183 - Policy, Evidence and Practice in Mental Health Care: Infant Mental Health
Bibliographic record
Abstract
Objectives Health care policies should be implemented to provide the proper care for children with mental health disorders and there is a need for improved infant and early childhood mental health assessment. In this paper we examine how system data reflecting program practice meet to inform advances in developing infant mental health policy. Methods Data from the Collaborative Mental Health Care (CMHC) program, a consultation based service in the focusing on the early identification of children (aged 0-5) at significant risk for developing mental health problems, was analyzed in comparison to those not coming into contact with such specialized services. Results Compared to others of the same age, those with CMHC involvement waited less time [mean days 13.7 (S.D. 32.3) vs. mean days 69.3 (S.D. 180.3] and had shorter lengths of stay [mean days 139.9 (S.D. 119.3) vs. mean days 232.4 (S.D. 329.7] and proportionately fewer registrations. Conclusions Early identification of children's mental health concerns through supporting the community through specialized consultation and promoting resiliency can significantly reduce mental health service utilization by offering more specific and specialized information, referral or treatment services for infants and very young children. The policy implications are self-evident: All provinces require specialized mental health services for infants and very young children in order to better serve children and increase service capacity.
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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 | 0.052 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".