UTILIZING PATIENT AND HEALTHCARE PROVIDER FEEDBACK TO VALIDATE A CLINICAL SCREENING TOOL FOR IDENTIFYING AND ASSISTING PERPETRATORS OF VIOLENCE
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".