Identification of probable child maltreatment using prospectively recorded information between 5 months and 17 years in a longitudinal cohort of Canadian children
Bibliographic record
Abstract
Abstract Background Both prospective and retrospective measures of child maltreatment predict mental health problems, despite their weak concordance. Research remains largely based on retrospective reports spanning the entire childhood due to a scarcity of prospectively completed questionnaires targeting maltreatment specifically. Objective We developed a prospective index of child maltreatment in the Québec Longitudinal Study of Child Development (QLSCD) using prospective information collected from ages 5 months to 17 years and examined its concordance with retrospective maltreatment. Participants and Setting The QLSCD is an ongoing population-based cohort that includes 2,120 participants born from 1997-1998 in the Canadian Province of Quebec. Methods As the QLSCD did not have maltreatment as a focal variable, we screened 29,600 items completed by multiple informants (mothers, children, teachers, home observations) across 14 measurement points (0-17 years). Items that could reflect maltreatment were first extracted. Two maltreatment experts reviewed these items for inclusion and determined cut-offs for possible child maltreatment. Retrospective maltreatment was self-reported at 23 years. Results Indicators were derived across preschool, school-age and adolescence periods and by the end of childhood and adolescence, including presence (yes/no), chronicity (re-occurrence), extent of exposure and cumulative maltreatment. Across all developmental periods, the presence of maltreatment was as follows: physical abuse (16.3-21.8%), psychological abuse (3.3-21.9%), emotional neglect (20.4-21.6%), physical neglect (15.0-22.3%), supervisory neglect (25.8-44.9%), family violence (4.1-11.2%) and sexual abuse (9.5% in adolescence only). Conclusions In addition to the many future research opportunities offered by these prospective indicators of maltreatment, this study offers a roadmap to researchers wishing to undertake a similar task. Highlights In this longitudinal cohort, maltreatment experts retained 251 of 29,600 items available Probable maltreatment indicators were derived: presence, chronicity, extent of exposure, and cumulative maltreatment Prevalence rates vary from 3.3% and 44.9% across developmental periods, and 16.5-67.3% by the end of adolescence Prospective and retrospective maltreatment identify different groups of individuals As most studies use retrospective data, findings suggest that the representation of child maltreatment is incomplete and retrospective reports should be complimented by prospective data, whenever possible
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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".