Medical neglect in Ontario: Implications for health care provision
Bibliographic record
Abstract
OBJECTIVES: This study explores child welfare investigations for medical neglect in Ontario, Canada, focusing on household, family and child characteristics of such investigations and factors associated with substantiated victimization. METHODS: This analysis used data from the Ontario Incidence Study of Reported Child Abuse and Neglect 2018. Bivariate analyses compared medical neglect with other neglect investigations to create a profile of medical neglect investigations in Ontario, and a binary logistic regression determined which case characteristics were 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 and caregivers under 21 years old, households that had run out of money in the past 6 months for basic necessities, primary caregivers with few social supports, mental health issues or drug/solvent abuse concerns, and children with at least one functioning concern. Medical neglect investigations in which the primary caregiver had few social supports were almost four times more likely to be substantiated (OR=3.698, P<0.05). CONCLUSIONS: While the public's perception of medical neglect tends to focus on parental refusal of treatment due to philosophical/religious beliefs, this Ontario sample indicates that medical neglect is often driven by financial constraints and a lack of social support. Implications for health care providers within a universal health care system 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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".