31 Identifying Child Maltreatment in Virtual Medical Appointments - What Are We Missing?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».