Knowledge Translation of the Woman Abuse Screening Tool
Bibliographic record
Abstract
Context In academia, dissemination of study results has often been the final step of the research process. However, more recently, knowledge translation defined as the flow and uptake of research knowledge has gained prominence. This study describes the knowledge translation of the Woman Abuse Screening Tool (WAST), developed to identify women at risk of or being abused for use in the family practice context. Objective To evaluate the knowledge translation of the WAST between 2000 to 2024. Study Design and Analysis We assessed the knowledge translation of the WAST by examining the number of requests received during this period, date of the request, country of origin, discipline of the researcher, and whether there had been a request to translate the WAST. We also conducted a Google Scholar search, using the search term WAST between 2000 and 2024 to determine the number of publications citing the WAST. Setting Academic Research Centre. Population Studied Requests from individuals seeking permission to use the WAST. Intervention/Instrument the Woman Abuse Screening Tool. Outcome Measures N/A. Results There have been 81 requests for permission to use the WAST. There was a notable increase in requests for permission to use the WAST during and after the onset of the COVID-19 pandemic. Twenty-six were received from researchers in the USA, 12 in Peru, 5 each came from Canada, Malaysia and Turkey. One request each came from 11 different countries (e.g. Korea, Australia), 2 – 4 requests came from 17 other countries (e.g. Spain, Mexico). Requests to use the WAST have come primarily from the disciplines of family medicine, nursing, psychology, public health, and social work. Eight researchers asked for permission to translate the WAST into the dominant language of the population, including Italian, Greek, Portuguese, and Icelandic. We also received 6 requests for translation of the WAST into Indigenous or local dialects. The Google search for publications citing the WAST between 2000 and 2024 identified 245 references, with 61 in the last 4 years. Conclusions These results illustrate successful knowledge translation. While the WAST was originally developed for use in the family practice context, these findings demonstrate how the WAST crosses disciplinary boundaries, has a global impact and continues to be a relevant and useful clinical and research tool.
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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 | 0.070 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".