Computer-assisted screening for intimate partner violence in family practice
Bibliographic record
Abstract
The RCT participants in the intervention arm completed a computer-based health risk assessment before seeing their physician. The computer survey included questions on IPV and other health risks. Computer-generated risk reports were attached to patients' medical charts for review by their physician. Participants in the control arm received standard medical care. Data were collected by audio taping the medical visits and an exit survey of patients. The results reveal that the overall prevalence of IPV in the family practice was high; 14.6% in the pencil and paper survey and 13.7% in the RCT study. The RCT demonstrated that, compared to standard medical care, the computer-assisted screening resulted in a 1.8-fold increase in the likelihood of physician and patients discussing IPV risk and the detection of women at risk of IPV increased by a factor of 2.5-fold. Further, RCT participant patients showed acceptance of the computer-assisted screening, after its use. The interviewed physicians perceived computer-assisted screening as most feasible for periodic health exams and follow-up visits, given the existing logistical limitations. Overall, physicians expressed general acceptance of the computer screening. The results provide impetus for improvements in clinical practice, medical training, policy and research to address the issue of IPV. The use of the screening tool may be enhanced by continued research into its generalizability by inclusion of multiple family practice clinics and various primary care settings. The overall aim of the dissertation research was to advance knowledge on the phenomenon of computer-assisted screening for intimate partner violence (IPV) within a Canadian family practice context. A series of inter-linked studies (quantitative and qualitative) was conducted to answer three research questions: (1) what is the effectiveness of computer-assisted screening for increasing physician-patient discussions on and detection of IPV risk? (2) what is the acceptance of computer-assisted screening among patients? and (3) what is the feasibility of computer-assisted screening from the perspectives of family physicians? To answer the first two questions, a randomized controlled trial (RCT) was conducted at an academically-affiliated family practice clinic in Toronto with adult female patients in a current or recent intimate relationship (n = 314). The RCT planning was facilitated by studies in the pre-trial phase (a pencil and paper survey of patients, and a psychometric scale validation). To answer the third question, in-depth qualitative interviews were conducted with the trial physicians (n = 10).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".