The determinants of service complexity in children with intellectual disabilities
Bibliographic record
Abstract
BACKGROUND: To date, little is known about the predictors of healthcare service utilisation in children with intellectual disability (ID). The aim of this study was to identify the factors associated with service complexity in children with ID in Ontario, Canada. METHODS: The population of this cross-sectional study consisted of 330 children with ID ages 4 to 18 years who accessed mental health services from November of 2012 to June of 2016 in four agencies. All participants completed the interRAI Child and Youth Mental Health and Developmental Disability Assessment Instrument, which is a semi-structured clinician-rated assessment that covers a range of common issues in children with ID. The outcome of this study was a service complexity variable based on (1) mental health service utilisation including any services provided to the child and (2) the management involved in providing that care. Eight individual items were summed, resulting in a scale that ranged from 0 to 8. Scores were then dichotomised into two groups: a score of 0-2 identified children with a low service complexity and a score of 3 or higher identified children with a high service complexity. RESULTS: After adjustment for other covariates, gender was not associated with service complexity. Children aged 11-14 years and children with autism spectrum disorder used over twofold higher levels of service complexity than children aged equal to or less than 10 years or children with other causes of ID. Moreover, victims of bullying, high scores on the family functioning scale or learning or communication disorder were associated with greater service complexity. CONCLUSIONS: The findings of this study indicate that a variety of factors are related to service complexity ranged from children's nonclinical (age and experiences of bullying) to clinical (e.g. aggression, learning/communication problems and autism spectrum disorder) characteristics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".