Risk for intimate partner violence : an investigation of the psychometric properties of the Spousal Assault Risk Assessment Guide in a New Zealand population : a thesis presented in partial fulfilment of the requirements for the degree of Doctorate of Clinical Psychology at Massey University, Wellington, New Zealand
Bibliographic record
Abstract
International and national studies have consistently shown intimate partner \nviolence is a common phenomenon that cuts across all societies, education and \nsocioeconomic levels, and ethnic and cultural groups. The impact of which includes \nnegative physical and mental health consequences for the victims. Risk assessments \nmay play a role in assisting the management and/or prevention of harm. Assessment of \nan offender’s risk of future violence play a central role in decision making pertaining to \nthat person’s sentencing, community release, case management, and public safety \nconcerns. Yet the assessments also need to ensure that the rights of the individual being \nassessed are not violated by misclassification. One method for addressing this issue is to \nensure that risk assessment measures are accurate, that is, the measure is reliable and \nvalid. In New Zealand to date, no intimate partner violence risk assessment tools have \nbeen evaluated. The current study, therefore, aims to fill this void by investigating the \nreliability and validity of the Spousal Assault Risk Assessment (SARA) guide. This was \nachieved in three parts, using a sample of 43 men recruited from community based \nstopping violence programmes. Part One evaluated the internal consistency and \ninterrater reliability of the SARA, Part Two evaluated the convergent and discriminant \nvalidities, and Part Three, which employed a prospective design with 36 participants \nfrom the total sample, evaluated the predictive validity and incremental validity of the \ndynamic risk factors. The findings indicated that while the internal consistency, and \nconvergent, discriminant, and predictive validates were adequate, the dynamic risk \nfactors did not evidence incremental validity over the static risk factors, and the \ninterrater reliability was variable. In addition, it was found that the source of \ninformation provided to the observers impacted on the resulting agreement coefficients. \nTherefore, before the SARA is implemented as a risk assessment measure in New \nZealand methods for improving the interrater reliability and exploration of the \nusefulness of the dynamic risk factors in reducing risk should be explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".