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Record W4416207456 · doi:10.1302/1358-992x.2025.13.111

UTILIZING PATIENT AND HEALTHCARE PROVIDER FEEDBACK TO VALIDATE A CLINICAL SCREENING TOOL FOR IDENTIFYING AND ASSISTING PERPETRATORS OF VIOLENCE

2025· article· en· W4416207456 on OpenAlexaboutno aff
Tanya Cherppukaran, Kaitlyn Dillabough, Golpira Elmi Assadzadeh, Brienne McLane, Claire Temple‐Oberle, Pia Schneider

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceHealth careLikert scaleIdentification (biology)Poison controlHuman factors and ergonomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.058
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.070
GPT teacher head0.393
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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