Child Maltreatment-Related Investigations Involving Infants: Opportunities for Resilience?
Bibliographic record
Abstract
Objective: To examine child welfare cases involving infants (less than 1 year old) and identify factors predicting service provision at the conclusion of a maltreatment-related investigation. Method(s): This study involves a secondary analysis of the 2008 Ontario Incidence Study of Reported Child Abuse and Neglect (OIS-2008). Bivariate and multivariate analyses were conducted to identify the profile of investigations involving infants (n=538) and the factors predictive of the decision to transfer a case to ongoing services at the conclusion of the investigation, rather than close the case postinvestigation. Results: Primary caregiver functioning concerns emerged as the strongest predictor of the decision to transfer a case to ongoing service across different case referral sources. These included: cognitive impairment, victim of intimate partner violence (IPV), few social supports, drug/solvent abuse, mental health issues, and caregivers under the age of 21. Infant functioning (e.g., attachment issues, developmental delay) and investigation type (maltreatment or risk of maltreatment) did not predict ongoing service provision. Conclusions and Implications: The functioning of the caregiver is the strongest determinant of ongoing child welfare involvement with infants, with different caregiver vulnerabilities emerging as more salient depending on the type of referral sources (hospital; police; social services; non-professional community). Infant investigations involve mostly young primary caregivers who struggle with poverty, single-parenthood, lack of social supports, mental health issues, and intimate partner violence. Implication: Given the multi-problem experience of caregivers, prevention of maltreatment recurrence need to reflect multi-sector collaboration in order to promote infant health and caregiver resiliency. Infant functioning may be an under-considered domain among workers investigating maltreatment and may, therefore, limit the opportunity for resilience, including developmental recovery and issue-specific interventions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".