Prevalence of Mental Disorders and Associated Service Variables among Ontario Children Who are Permanent Wards
Bibliographic record
Abstract
OBJECTIVE: To identify the prevalence rate of mental disorders among Ontario children who are permanent wards and also the key practice and descriptive variables associated with their diagnostic status. METHOD: I reviewed case files from a stratified random sample of 429 Ontario children who were permanent wards with no access to biological parents on December 31, 2003. Data abstracted from files included information on descriptive variables (such as age, sex, and type of permanent ward), all disorders (that is, mental and other current medical diagnoses and disabilities), family history, maltreatment experiences, service history (such as age at admission to care and current residential placement type), and permanency plans. RESULTS: The prevalence of mental disorders was 31.7%. A significantly higher proportion of children with mental disorders experienced maltreatment. Children with mental disorders were almost 3 times more likely than those without mental disorders to be placed by Children's Aid Societies in privately operated resources, such as group homes, and almost 10 times less likely to be living in a probationary adoption home. Although children with mental disorders were less likely to have a permanency plan of adoption than were children without mental disorders, regression analysis found that only 2 variables--age on becoming a permanent ward and age at the time of the study--were predictive of children's adoption plans. CONCLUSIONS: The findings support the need for improved monitoring of the aggregate mental health needs of children who are permanent wards. Numerous implications for service delivery and future research are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".