Characteristics and Service Needs of Maltreated Children Referred for Mental Health Services at a Child Advocacy Centre in Canada.
Bibliographic record
Abstract
OBJECTIVE: There is a dearth of Canadian-based literature on children referred to treatment services following maltreatment exposure. In order to inform assessment, intervention, and program development to improve outcomes, insight into the demographics and mental health needs of this population is required. METHODS: A retrospective file review of 176 children and youth who were referred for assessment and treatment at a mental health partner agency within a Canadian Child Advocacy Centre was conducted from January 2016 to June 2017. A standardized protocol was developed to extract data on family and child demographic characteristics, type of maltreatment, other adversity exposure, presenting concerns of the child, and mental health service utilization. RESULTS: The majority of children were female (66.5%), 4.5% were 0 to <5 years, 66.5% were 5 to <13 years, and 29.0% were 13 to <18 years of age. More than half of the children (53.4%) had multiple forms of maltreatment, with 67% exposed to sexual abuse. Exposure to other forms of adversity was also common, including domestic violence (53.4%) and parental mental health difficulties (52.3%). Most children had more than five presenting concerns at the time of referral, and most went on to receive intervention services. Sixty-nine percent of families had not previously received child mental health treatment, although 41.5% had prior child welfare involvement. Thirty percent of families ended treatment prematurely. CONCLUSIONS: The current study illustrates the complex profile and mental health needs of children referred for treatment following maltreatment exposure. Results may have implications for clinical care improvement that support maltreated children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 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".