Psychometric properties of the Spousal Assault Risk Assessment from samples of people having perpetrated intimate partner violence
Bibliographic record
Abstract
Since it was first published in 1995, the Spousal Assault Risk Assessment (SARA) Guide has become one of the most used and researched intimate partner violence (IPV) risk measures worldwide. Yet, no recent review has formally and systematically established the psychometric properties of this measure. Furthermore, the third version of the SARA (SARA-V3) was published in 2015, with no psychometric critique to date. This review aimed to provide an inclusive and exhaustive literature review of all psychometric properties (i.e., predictive validity, convergent validity, internal consistency, and inter-rater) of the SARA, including V3. A systematic search of 17 databases was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Academic journals, book chapters, and gray literature were included but conference presentations were not. To be included studies had to report a psychometric property of any version of the SARA and be composed of individuals having committed IPV. The search identified 28 records published between 1997 and 2022. Results showed that although the literature on the SARA is mostly positive, it is much more varied in terms of both results and research quality than its widespread implementation might suggest. Most studies were conducted using case files in a research context with non-diverse samples, undermining ecological validity. Results for convergent and predictive validity were mostly positive. However, reliability statistics were under-researched and showed poorer results. Lastly, little research has gone into validating the SARA-V3, with what is available suggesting poorer reliability and validity than its predecessor. Practitioners are cautioned against transitioning to the newer version before further validation research has occurred.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".