Identifying child maltreatment during virtual medical appointments through the COVID-19 pandemic
Bibliographic record
Abstract
Background: Throughout the COVID-19 pandemic there has been a documented decline in reports to child protective services, despite an increased incidence of child maltreatment. This is concerning for increasing missed cases. This study aims to examine if and how Canadian paediatricians are identifying maltreatment in virtual medical appointments. Methods: A survey was sent through the Canadian Paediatric Surveillance Program (CPSP) to 2770 practicing general and subspecialty paediatricians. Data was collected November 2021 to January 2022. Results: With a 34% (928/2770) response rate, 704 surveys were eligible for analysis. At least one case of child maltreatment was reported by 11% (78/700) of respondents following a virtual appointment. The number of cases reported was associated with years in medical practice (P = 0.026) but not with the volume (P = 0.735) or prior experience (P = 0.127) with virtual care, or perceived difficulty in identifying cases virtually (Cramer's V = 0.096). The most common factors triggering concern were the presence of social stressors, or a clear disclosure. The virtual physical exam was not contributory. Nearly one quarter (24%, 34/143) required a subsequent in-person appointment prior to reporting the case and 32% (207/648) reported concerns that a case had been identified late, or missed, following a virtual appointment. Some commented that clear harm resulted. Conclusions: Many barriers to detecting child maltreatment were identified by paediatricians who used virtual care. This survey reveals that virtual care may be an important factor in missed cases of child maltreatment and may present challenges to timely identification.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".