What do we know about interactive computer-assisted screening for intimate partner violence and control in clinical settings? A systematic review
Bibliographic record
Abstract
Background: Intimate partner violence is a major public health issue, particularly among women. Abused women experience many acute and chronic health consequences resulting in frequent healthcare visits. There exists a system-level opportunity to intervene, yet abused women refrain from spontaneous disclosure of their experiences of victimization due to embarrassment. Meanwhile providers often fail to ask due to lack of time, priority of acute medical problems and discomfort. Missed opportunities to detect intimate partner violence and control (IPVC) can be availed by computer-assisted interactive screening. Aim: The purpose of this paper is to critically review current scientific knowledge on the use of enhanced Web 2.0 interactive computer-assisted screening for IPVC in clinical settings. Methods: A systematic review of peer-reviewed published literature was conducted using Medline and PsychInfo data bases from 1996 to 2010. Eligibility criteria were applied to the identified records. Additional studies were identified by searching reference list and contacting authors. Eight eligible studies were appraised for the study characteristics and IPVC related outcomes for the process-of-care, patient, and provider. Results: The selected studies (descriptive, randomized trial, and qualitative) were conducted in the emergency and family medicine settings on two programs of research which used similar interactive computer screen, Promote Health. The reviewed evidence supports the effectiveness of computer screening for improving provider-patient communication on IPVC in both settings and compromised mental health in family medicine. However the management of detected cases of IPVC by time-pressed frontline clinicians needs a more supportive environment. The need for such system-level support is greater for the emergency setting. Conclusions: The use of computer-assisted screening in similar settings can enhance the detection and disclosure of IPVC, although a coordinated multiservice response is needed to address it comprehensively. Future studies should examine the development of a coordinated response and the role of context on the success or failure of such program.
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 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 | 0.032 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".