A 40-year study of child maltreatment over the early life course predicting psychiatric morbidity, using linked birth cohort and administrative health data: protocol for the Childhood Adversity and Lifetime Morbidity (CALM) study
Bibliographic record
Abstract
BACKGROUND: Child maltreatment is a major public health issue worldwide. Retrospective studies show a strong association between self-reported child maltreatment and poor mental and physical health problems. Prospective studies that use reports to statutory agencies are less common, and comparisons of self- and agency-reported abuse in the same cohort even rarer. AIMS: = 7223) from Brisbane in Queensland, Australia (including notifications to child protection agencies), to compare psychiatric outcomes in adulthood of agency- and self-reported child maltreatment while minimising attrition bias. METHOD: We will compare people with all forms of self- and agency-reported child maltreatment to the rest of the cohort, adjusting for confounding in logistic, Cox or multiple regression models based on whether outcomes are categorical or continuous. Outcomes will be hospital admissions, emergency department presentations or community/out-patient contacts for ICD-10 psychiatric diagnoses, suicidal ideation and self-harm as recorded in the relevant administrative databases. CONCLUSIONS: This study will track the life course outcomes of adults after having experienced child maltreatment, and so provide an evidence-based understanding of the long-term health and behavioural consequences of child maltreatment. It will also consider health outcomes that are particularly relevant for adolescents and young adults, especially in relation to prospective notifications to statutory agencies. Additionally, it will identify the overlap and differences in outcome for two different sources of child maltreatment identification in the same cohort.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".