Hospital admissions and community health service contacts for mental illness following self-reported child maltreatment: Results from the Childhood Adversity and Lifetime Morbidity (CALM) study
Bibliographic record
Abstract
BACKGROUND: Child maltreatment (CM) includes neglect, and several types of abuse, including physical, emotional, and sexual. CM has been associated with a wide range of mental illnesses. Literature examining these illnesses in mid-life is scarce, and the impact of these illnesses on mental health service use is currently unknown. OBJECTIVE: To examine associations between self-reported CM and subsequent hospital admissions for mental illnesses, and/or community mental health service contacts. SETTING: Birth cohort study data linked to administrative health data, including hospital admissions and community mental health service contacts, up to the age of 40. METHODS: Associations between hospital admissions for mental health and community mental health contacts and CM subtypes (neglect, physical abuse, emotional abuse and sexual abuse) were examined using multivariate logistic regression. RESULTS: Adjusted analyses showed that all subtypes of CM were significantly (p < 0.05) associated with admissions to hospital for any type of mental illness (aOR range 1.87-3.61), non-psychotic mental disorders (aOR range 1.98-3.61), alcohol and/or substance use (aOR range 2.83-5.43), and community mental health service contacts (aOR range 2.44-3.13). Hospital admissions for psychotic mental disorders were significantly associated with physical abuse, emotional abuse, and sexual abuse (aOR range 2.14-3.93). CONCLUSIONS: The results of this study confirm the current knowledge around CM and subsequent mental health illnesses up to the age of 40, and extend this knowledge to hospital and mental health 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".