Étude des paramètres d'un baromètre de dépistage des violences conjugales comparativement au WAST
Bibliographic record
Abstract
Introduction: the means of screening for domestic violence in general medicine in France are limited. The Women abuse screening tool (WAST) questionnaire (Canadian questionnaire with 8 questions) was adapted in French and showed its effectiveness in the screening of domestic violence. It became the reference questionnaire in France. In 2019, the various cities in France set up a barometer of domestic violence. This barometer is presented as a tool for awareness and prevention aimed at promoting freedom of speech and facilitating reports of domestic violence. However, no studies have been conducted on the effectiveness of the barometer in screening for domestic violence compared to the WAST reference questionnaire. Purpose of research: the main objective of this study is to study the sensitivity and specificity of this barometer compared to the WAST questionnaire to know if this barometer can be used as a screening tool for domestic violence in general practice in the region PACA. Results: 140 patients participated in the study. 94 (67.2%) women and 46 (32.8%) men. 13 patients obtained a positive WAST questionnaire (estimated prevalence of 9.286%), including 12 women. 17 patients were considered in the orange or red zone of the barometer (an estimated prevalence of 12.142%). These results allowed us to obtain a sensitivity of 92.3% and a specificity of 96.1% for the barometer. A positive predictive value of 70.58% and a negative predictive value of 99.18%. The average time of the barometer was less than 15 seconds, and its acceptability of 8.943 was 10. Conclusions: the barometer has a good sensitivity and specificity compared to WAST, it is mostly well accepted by patients. This is an additional argument for using the barometer as a systematic screening tool. This test could be applicable to the common practice of general medicine, but requires an assessment of its wider use.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".