31 Identifying Child Maltreatment in Virtual Medical Appointments - What Are We Missing?
Bibliographic record
Abstract
Abstract Background Throughout the COVID-19 pandemic, concerns have emerged regarding missed cases of child maltreatment. Evidence suggests an increased incidence of child maltreatment despite a documented decline in reports to child protective services. In Ottawa, reports dropped by 30-40% at the start of the pandemic in 2020. Pediatricians play an important role in the detection of child maltreatment and many have shifted from in person to virtual care. However, there is a paucity of published literature on this topic. We hypothesize that the shift to virtual visits is a barrier to identifying cases of child maltreatment and may contribute to missed cases. Objectives Our survey assesses if and how Canadian pediatricians are identifying child maltreatment over virtual medical appointments, as well as the barriers and enabling factors to doing so. Design/Methods The Canadian Paediatric Surveillance Program (CPSP) is a joint effort with the Canadian Paediatric Society and Public Health Agency of Canada towards national pediatric surveillance through monthly surveys to 95% of Canadian pediatricians. Using their infrastructure, a one-time survey was sent to 2770 pediatricians between November 2021 and January 2022 with data analyzed for qualitative themes and descriptive statistics. Results There was a 34% response rate (n= 928) and 704 valid responses. Exclusions were for no provision of virtual care, incomplete surveys or no reported cases of child maltreatment in their career (n=93, 10%). The average number of years in independent practice was 17.5 years, and 69% had not provided virtual care prior to the pandemic. Based on a virtual visit, at least one case of child maltreatment was reported by 16% of physicians prior to the pandemic, and by 11% following March 2020. Nearly one-third (30%) of these cases required a subsequent in-person visit prior to making the report. Social stressors and clear disclosures from patients and caregivers were the main factors leading to reports. The virtual physical exam was not a factor that triggered concerns of maltreatment in any case. Respondents reported at a rate of 68% that it was slightly or much more difficult to detect child maltreatment over virtual visits. Concerns that a case had been missed or identified late in association with virtual care were reported by 29% of physicians (n=206) with some commenting that clear harm resulted. Conclusion This survey shows that virtual medical care presents barriers to identifying child maltreatment and may be an important factor in missed cases of child maltreatment.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".