Sentinel surveillance of child maltreatment cases presenting to Canadian emergency departments
Bibliographic record
Abstract
BACKGROUND: The Canadian Hospitals Injury Reporting Prevention Program (CHIRPP) is a sentinel surveillance program that collects and analyzes data on injuries and poisonings of people presenting to emergency departments (EDs) at 11 pediatric and eight general hospitals (currently) across Canada. To date, CHIRPP is an understudied source of child maltreatment (CM) surveillance data. This study: (1) describes CM cases identified in the CHIRPP database between1997/98 to 2010/11; (2) assesses the level of CM case capture over the 14-year period and; (3) uses content analysis to identify additional information captured in text fields. METHODS: We reviewed cases of children under 16 whose injuries were reported as resulting from CM from 1997/98 to 2010/11. A time trend analysis of cases to assess capture was conducted and content analysis was applied to develop a codebook to assess information from text fields in CHIRPP. The frequency of types of CM and other variables identified from text fields were calculated. Finally, the frequency of types of CM were presented by age and gender. RESULTS: A total of 2200 CM cases were identified. There was a significant decrease in the capture of CM cases between 1999 and 2005. Physical abuse was the most prevalent type (57%), followed by sexual assault (31%), unspecified maltreatment (7%), injury as the result of exposure to family violence (3%) and neglect (2%). Text fields provided additional information including perpetrator characteristics, the use of drugs and/or alcohol during the injury event, information regarding the involvement of non-health care professionals, whether maltreatment occurred during a visitation period with a parent and, whether the child was removed from their home. CONCLUSIONS: The findings from this initial study indicate that CHIRPP could be a complimentary source of CM data. As an injury surveillance system, physical abuse and sexual assault were better captured than other types of CM. Text field data provided unique information on a number of additional details surrounding the injury event, including risk factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".