UTILIZING PATIENT AND HEALTHCARE PROVIDER FEEDBACK TO VALIDATE A CLINICAL SCREENING TOOL FOR IDENTIFYING AND ASSISTING PERPETRATORS OF VIOLENCE
Notice bibliographique
Résumé
Intimate partner violence (IPV) is the most common cause of nonfatal injury in women worldwide. Addressing IPV in healthcare has previously focused on survivors, with identification and assistance for the perpetrators often overlooked. Although screening tools to identify perpetrators exist, one that sensitively queries potential perpetrators of IPV does not exist. Therefore, we have co-designed patient-informed, non-accusatory screening questions to develop a brief screening tool with acceptable language. This tool would be valuable in effectively identifying and assisting IPV perpetrators and ultimately reducing IPV. This study aimed to evaluate the most acceptable screening questions for identification of perpetrators of IPV in fracture and hand clinics. We co-developed 12 patient-informed IPV screening questions and performed item reduction based on the most acceptable screening questions using a cross-sectional survey of male orthopaedic patients and healthcare providers (HCPs). The survey was electronically distributed to all eligible patients through orthopaedic hand and trauma clinics. Additionally, the survey was provided to members of the Canadian Orthopaedic Association and Canadian Society of Plastic Surgeons through SurveyMonkey (Momentive Inc, San Mateo, California, USA). We measured the acceptability of the 12 patient-informed screening questions on a 5-point Likert scale, ranging from Very Acceptable (1) to Very Unacceptable (5). The acceptability of each question in each group was measured as the sum of scores given by all subjects divided by total score possible. Item reduction was then performed based on the most acceptable questions, such that the five highest-performing sample questions were selected amongst HCPs and patients. All statistical analysis was conducted using Python 3.7 (Python 3 Reference Manual, 2009) A total of 141 HCP (61% male, 36% female, and 3% other/prefer not to disclose) and 231 patient responses (71% lower extremity fractures and 29% upper extremity fractures) were analyzed. Orthopaedic patients, on average, had a significantly higher acceptability rating for each question compared to healthcare providers (all p < 0 .0001). Notably, three of the top five questions remained the same between the two groups, with the highest performing question being the same. The question that garnered the highest acceptability rating was, “Have you ever felt that you might need help with your anger?”. This study supports the continued development and validation of a novel screening tool that will effectively identify IPV perpetrators with more acceptable language than the current standard and with minimal time required in a busy clinical setting. Additionally, the high acceptability rating provided by patients signifies their comfort with the IPV screening questions that we have developed. Our goal is to identify IPV perpetrators in healthcare settings, thus facilitating guidance toward education and assistance in addressing their violent behaviour.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».