Examining the Relationship between Economic Hardship and Child Maltreatment Using Data from the Ontario Incidence Study of Reported Child Abuse and Neglect-2013 (OIS-2013)
Bibliographic record
Abstract
There is strong evidence that poverty and economic disadvantage are associated with child maltreatment; however, research in this area is underdeveloped in Canada. The purpose of this paper is to examine the relationship between economic hardship and maltreatment for families and children identified to the Ontario child protection system for a maltreatment concern. Secondary analyses of the Ontario Incidence Study of Reported Child Abuse and Neglect-2013 (OIS-2013) were conducted. The OIS-2013 examines the incidence of reported maltreatment and the characteristics of children and families investigated by child welfare authorities in Ontario in 2013. Descriptive and bivariate chi-square analyses were conducted in addition to a logistic regression predicting the substantiation of maltreatment. In 9% of investigations, the household had run out of money for food, housing, and/or utilities in the past 6 months. Children in these households were more likely to have developmental concerns, academic difficulties, and caregivers with mental health concerns and substance use issues. Controlling for key clinical and case characteristics, children living in families facing economic hardship were almost 2 times more likely to be involved in a substantiated maltreatment investigation (OR = 1.91, p < 0.001). The implications in regard to future research and promoting resilience 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".