Acute and outpatient service utilisation prior to, during, and following enrolment in community‐based mental health treatment among children and youth in Central Ontario: A proof of concept for cross‐sectoral data linkage
Bibliographic record
Abstract
AIM: Given the fragmentation of mental health and addictions (MHA) services for children and youth, comprehensive data on utilisation patterns are lacking. We sought to describe MHA-related service use across the community, acute and outpatient sectors. METHODS: We used linked health-administrative data sets to identify a cohort of individuals aged <18 who received MHA treatment in a large community organisation in Ontario, Canada between 1 April 2007 and 31 March 2012. We described their socio-demographic characteristics, examined their MHA-related concurrent service use in acute care and outpatient physician settings (primary care providers, paediatricians and psychiatrists), and compared service utilisation prior to, during and following enrolment using Poisson regressions. RESULTS: Among 7285 children and youth receiving community MHA treatment, there were 481 concurrent MHA-related emergency department visits, 173 hospitalisations and 12140 outpatient physician visits. The average age at enrolment was 10.5 years, and 64% of clients were enrolled for ≥3 months. MHA-related emergency department use significantly declined from 1 year prior, compared to 1 year following receipt of community MHA treatment (112 vs. 82 visits per 1000 person-years, P < 0.001), particularly in females, ages 10-14, those living in higher-income neighbourhoods and urban areas, and those with anxiety disorders. MHA hospitalisations also declined (45 vs. 32, P < 0.001), while outpatient physician visits increased (1750 vs. 1874, P < 0.001). CONCLUSIONS: Our study suggests that community-based MHA treatment may be effective in diverting children and youth away from acute care and highlights the importance of data linkage as a means to better understand the complexity of cross-sectoral MHA service use.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".