Compliance with mandatory reporting of intimate partner violence among professionals in Norway
Bibliographic record
Abstract
BACKGROUND: Mandatory reporting is a common legislative preventative measure for several types of crimes, among them family violence and specifically intimate partner violence (IPV). Among the individuals who are mandated to report under the law are professionals working with IPV victims and perpetrators in their practice. However, little is known about which characteristics are associated with compliance with the mandatory reporting of IPV (MR-IPV) law, on the one hand, and choosing not to report IPV, on the other. METHODS: The current study sampled 357 professionals from 6 different agencies working with IPV victims and/or perpetrators. Six dichotomous outcome variables of compliance with MR-IPV and choosing not to report were analyzed by multiple logistic regression. The independent variables were professionals' perceptions and knowledge of MR-IPV, context and workplace conditions, and experience with IPV cases and risk assessment. RESULTS: Findings showed that risk of compliance with MR-IPV varied between complying with and without consent. Perceived applicability of MR-IPV for an IPV victim was the only variable that had significantly positively odds ratio for both compliance with and without consent. For choosing not to report, significant variables varied between whether the incident had taken place sometime throughout participants' careers or during the last year, and whether it concerned a victim or a perpetrator. However, knowledge of MR-IPV, experience with IPV cases, expectations of MR-IPV, perceived workplace time management, and perception of compliance were significant for choosing not to report. CONCLUSIONS: Knowledge of the characteristics that are associated with professionals' compliance with MR-IPV is essential to better understand the application of MR-IPV, to implement practice that is consistent with law, and ultimately to prevent IPV. Further research is needed to explore the context of compliance with MR-IPV.
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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 | 0.003 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".