Dépistage de la violence faite aux femmes. Epreuves de validation et de fiabilité d'un instrument de mesure français.
Bibliographic record
Abstract
OBJECTIVE: To replicate, in a Francophone community, our prior work determining the reliability and validity of the full Woman Abuse Screening Tool (WAST) and a two-item version (WAST-Short). DESIGN: Questionnaires completed by abused and nonabused women. SETTING: Two women's shelters in Francophone communities in Ontario and Quebec and participants' homes or workplaces. PARTICIPANTS: A convenience sample of 25 abused women currently residing in two women's shelters and a convenience sample of 21 women who reported they were not abused. MAIN OUTCOME MEASURES: Women's responses to French versions of the WAST, the Abuse Risk Inventory (ARI), and comfort in answering the questions were compared. Also, the reliability and validity of French versions of WAST and WAST-Short were assessed. RESULTS: Abused (n = 23) and not abused (n = 21) women were demographically similar. A strong single-factor structure that accounted for 81% of total variance in the French WAST items was identified. The French WAST was found to be highly reliable with a coefficient alpha of .95 and demonstrated construct and discriminant validity. The WAST-Short correctly classified all the nonabused women and 78.7% of the abused women. The abused women reported feeling less comfortable responding to the WAST questions than the nonabused women. CONCLUSION: The French version of the WAST demonstrated good reliability and validity and discriminated between known samples of abused and nonabused women. Even though the French WAST-Short did not perform as well as the English version, results of this study support further evaluation of the WAST for screening women in Francophone or bilingual family practice settings.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".