Medical neglect in Canada: a cross-sectional study exploring drivers of substantiation using a national dataset
Bibliographic record
Abstract
BACKGROUND: This study explores child welfare investigations for medical neglect in Canada, focusing on household, family and child characteristics and drivers of substantiated victimisation. METHODS: Data from the Canadian Incidence Study of Reported Child Abuse and Neglect 2019 (CIS-2019), the most recent source of Canadian data on child maltreatment-related investigations, were used. A multistaged sampling design was used in the CIS-2019 to select a representative sample of child welfare agencies, and data were collected from investigating child welfare workers at selected agencies over a 3-month sampling period. Complex survey design weights were used to derive an annual estimate of maltreatment-related investigations conducted in the country (299 171 investigations involving children aged 0-15 years). The current study specifically examined the characteristics of the estimated 2934 investigations for medical neglect in the CIS-2019 (1% of all investigations). Bivariate analyses compared medical neglect investigations with investigations involving other forms of neglect, and a binary logistic regression identified characteristics associated with substantiation of medical neglect. RESULTS: Compared with other neglect investigations, medical neglect investigations were more likely to involve children less than 1 year old, caregivers under 21 years old and over 30 years old, households whose primary source of income was full-time work, primary caregivers with mental health concerns, and children with at least one functioning concern. Medical neglect investigations in which the primary caregiver had noted alcohol abuse (OR=4.693, p<0.001), drug/solvent abuse (OR=2.485, p<0.001) or mental health concerns (OR=2.231, p<0.001) were more likely to be substantiated. CONCLUSIONS: Medical neglect is a child welfare concern with potentially dire consequences. We demonstrate that substantiated cases of medical neglect in Canada arise within the context of complex caregiver and child factors. Early collaboration, preventative efforts and supportive relationships between families and their healthcare teams could enhance adherence to medical recommendations and mitigate harm to the child.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".