Predicting patterns of service utilization within children’s mental health agencies
Bibliographic record
Abstract
BACKGROUND: Some children with mental health (MH) problems have been found to receive ongoing care, either continuously or episodically. We sought to replicate patterns of MH service use over extended time periods, and test predictors of these patterns. METHODS: Latent class analyses were applied to 4 years of visit data from five MH agencies and nearly 6000 children, 4- to 13-years-old at their first visit. RESULTS: Five patterns of service use were identified, replicating previous findings. Overall, 14% of cases had two or more episodes of care and 23% were involved for more than 2 years. Most children (53%) were seen for just a few visits within a few months. Two patterns represented cases with two or more episodes of care spanning multiple years. In the two remaining patterns, children tended to have just one episode of care, but the number of sessions and length of involvement varied. Using discriminant function analyses, we were able to predict with just over 50% accuracy children's pattern of service use. Severe externalizing behaviors, high impairment, and high family burden predicted service use patterns with long durations of involvement and frequent visits. CONCLUSIONS: Optimal treatment approaches for children seen for repeated episodes of care or for care lasting multiple years need to be developed. Children with the highest level of need (severe pathology, impairment, and burden) are probably best served by providing high intensity services at the start of care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".