Children’s exposure to intimate partner violence as a form of child maltreatment in Canada: Analysis of the Canadian incidence study of reported child abuse and neglect (CIS)
Bibliographic record
Abstract
Countries around the world have established child welfare systems to protect children from child maltreatment, prevent future maltreatment, and promote optimal child well-being. A better understanding of children’s child welfare involvement can aid in decision-making around policy directives, allocation of resources, and practice guidelines. In Canada, the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS) is the only source of nationally aggregated data about investigations conducted by child welfare organizations. Methods: We analyze the increase in child welfare investigations in Canada from 2008 to 2019 using data from the CIS-2008 and CIS-2019. Results: The rate of investigations in Canada for children’s exposure to IPV has increased from 6.84 per 1,000 children in 2008 to 9.50 in 2019. The rate of substantiation for children’s exposure to IPV has also increased between 2008 and 2019, from 4.86 per 1,000 to 6.51 per 1,000 children in Canada. When controlling for clinical characteristics of the child, primary caregiver, and household, investigations focused on exposure to IPV were nearly six times as likely to be substantiated compared to all other investigations (Odds Ratio [OR] = 5.789, 95 % CI [5.429, 6.590]). Conclusions: The more we widen the reasons for child welfare investigations, the more we investigate families experiencing other hardships that are not maltreatment, and likely better served by other sectors. The response going forward must be balanced with the tragic outcomes in cases of IPV, and the majority of cases seen by child welfare where there is no harm and the investigation is closed with no further child welfare services offered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".