Identifying the characteristics associated with intimate partner stalking: a mixed methods structured review and narrative synthesis
Bibliographic record
Abstract
The empirical research on the clinical management of intimate partner stalking perpetrators remains in the early stages of informing forensic practice. This study presents the first known structured review which seeks to inform intervention pathways for this group through illuminating the characteristics associated with intimate partner stalking. A systematic search was conducted across five academic databases, reference lists of papers were reviewed, and ‘experts’ contacted to identify relevant papers. The search strategy identified 2674 papers. Twenty-two studies were selected in line with predetermined inclusion/exclusion criteria and assessed for methodological quality. All studies employed an observational research design; eighteen quantitative, two qualitative, and two mixed methods design studies were included. Data were extracted and subjected to narrative synthesis. Overall, intimate partner stalking perpetrators presented with some similar characteristics to intimate partner violence perpetrators, whilst some characteristics were deemed more prevalent to intimate partner stalking perpetrators. The findings illustrate there are likely to be subtypes of intimate partner stalking perpetrators, requiring a bespoke approach to intervention. Limitations are presented and recommendations made for future research. The wider implications for forensic practice in informing the clinical management of this group and approaches to intervention are discussed.
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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 | 0.049 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".