Application of the Woman Abuse Screening Tool (WAST) and WAST-short in the family practice setting.
Bibliographic record
Abstract
BACKGROUND: Our study objectives were to assess the validity and reliability of the Woman Abuse Screening Tool (WAST) in the general population within the family practice setting; to determine the comfort levels of family physicians administering the WAST, their perceptions of its ability to help them identify abused women, and their willingness to continue using it in practice; and to determine the self-reported comfort of patients being asked the WAST questions by their family physicians. METHODS: We included a stratified random sample of 20 physicians practicing in both urban and rural settings drawn from 400 family physicians in London, Ontario, Canada, and the surrounding area. These physicians administered the WAST to 10 to 15 eligible and consenting patients during the course of regular care. Following the physician-patient encounter, patients were asked to complete both a measure about their comfort in being asked each of the WAST questions and the Abuse Risk Inventory (ARI). RESULTS: Scores on the WAST correlated well with those on the ARI. The reliability of the WAST among this sample was demonstrated by a coefficient alpha of 0.75. With the WAST-Short (the first 2 questions of the WAST), 26 of the 307 patients screened (8.5%) were identified as experiencing abuse. The physicians were comfortable administering the WAST to their women patients, and 91% of the patients reported being comfortable or very comfortable when asked the WAST questions by their family physician. CONCLUSIONS: The WAST was found to be a reliable and valid measure of abuse in the family practice setting, with both patients and family physicians reporting comfort with it being part of the clinical encounter.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".