85 Patterns of Child Maltreatment Referrals in Immigrant Populations
Bibliographic record
Abstract
Children from immigrant families are at risk for decreased visibility, social isolation, language barriers, cultural differences, and financial hardship, which are risk factors associated with child maltreatment. To date, these are the first published Canadian data on this subject. To identify incidence patterns of child maltreatment in immigrant populations in comparison with non-immigrant populations. This study is a 3-year (2014 – 2016) retrospective chart review of child maltreatment referrals at our tertiary institution. Immigrant families were identified with the “English as a Second Language” field on the intake questionnaire, as a proxy for semi-acculturation. Chi-squared or ANOVA tests were used for categorical statistical analysis. Of a total 764 new referrals, 139 (18%) were from immigrant families and 625 (82%) were from non-immigrant families. Physical abuse referrals occurred at higher frequency than sexual abuse referrals in the immigrant group (63% and 24%, respectively) compared to the non-immigrant group (44% and 32%, respectively) (p < 0.01). Referrals for children 10 years of age or older occurred at higher frequency in the immigrant group (21%) compared to the non-immigrant group (12%) (p < 0.01), for which subgroup analysis showed a similar pattern in physical abuse referrals but not sexual abuse referrals (p < 0.01). Only 7 of 67 (10%) physical abuse referrals had claims of discipline based on different cultural values. A majority (54%) of immigrant referrals were assessed by a physician prior to referral, most commonly an emergency physician (74%), which was higher than disclosures to teachers (5%). Major risk factors affecting immigrant families include needing an interpreter (37%), the child being invisible in the community (34%), previous child protection agency involvement (21%), domestic violence (17%), financial stress (17%), and the family being socially isolated (16%). Ninety-six of 129 (74%) immigrant referrals were from postal codes with child poverty rates higher than the metropolitan average. Immigrant populations in this study have a different pattern of child maltreatment referrals, which may be attributable to a higher proportion of physical abuse referrals in older school-aged children. Physicians are an important gateway for detection of possible child maltreatment. Addressing common risk factors, such as language and visibility, may be useful in future screening and prevention efforts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".