Iskanje po katalogu digitalne knjižnice

Opis: Background: Domestic violence (DV) is a significant public health concern affecting the physical and mental well-being of numerous women, imposing a substantial health care burden. However, women facing DV often encounter barriers to seeking in-person help due to stigma, shame, and embarrassment. As a result, many survivors of DV turn to online health communities as a safe and anonymous space to share their experiences and seek support. Understanding the information needs of survivors of DV in online health communities through multiclass classification is crucial for providing timely and appropriate support. Objective: The objective was to develop a fine-tuned large language model (LLM) that can provide fast and accurate predictions of the information needs of survivors of DV from their online posts, enabling health care professionals to offer timely and personalized assistance. Methods: We collected 294 posts from Reddit subcommunities focused on DV shared by women aged ≥18 years who self-identified as experiencing intimate partner violence. We identified 8 types of information needs: shelters/DV centers/agencies; legal; childbearing; police; DV report procedure/documentation; safety planning; DV knowledge; and communication. Data augmentation was applied using GPT-3.5 to expand our dataset to 2216 samples by generating 1922 additional posts that imitated the existing data. We adopted a progressive training strategy to fine-tune GPT-3.5 for multiclass text classification using 2032 posts. We trained the model on 1 class at a time, monitoring performance closely. When suboptimal results were observed, we generated additional samples of the misclassified ones to give them more attention. We reserved 184 posts for internal testing and 74 for external validation. Model performance was evaluated using accuracy, recall, precision, and F1 -score, along with CIs for each metric. Results: Using 40 real posts and 144 artificial intelligence–generated posts as the test dataset, our model achieved an F1 -score of 70.49% (95% CI 60.63%-80.35%) for real posts, outperforming the original GPT-3.5 and GPT-4, fine-tuned Llama 2-7B and Llama 3-8B, and long short-term memory. On artificial intelligence–generated posts, our model attained an F1 -score of 84.58% (95% CI 80.38%-88.78%), surpassing all baselines. When tested on an external validation dataset (n=74), the model achieved an F1 -score of 59.67% (95% CI 51.86%-67.49%), outperforming other models. Statistical analysis revealed that our model significantly outperformed the others in F1 -score (P=.047 for real posts; P<.001 for external validation posts). Furthermore, our model was faster, taking 19.108 seconds for predictions versus 1150 seconds for manual assessment. Conclusions: Our fine-tuned LLM can accurately and efficiently extract and identify DV-related information needs through multiclass classification from online posts. In addition, we used LLM-based data augmentation techniques to overcome the limitations of a relatively small and imbalanced dataset. By generating timely and accurate predictions, we can empower health care professionals to provide rapid and suitable assistance to survivors of DV.
Ključne besede: domestic violence, online health communities, large language models, generative artificial intelligence, artificial intelligence
Objavljeno v DKUM: 22.07.2025; Ogledov: 0; Prenosov: 4
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Opis: Purpose: This paper analyses the theoretical, legislative and practical advantages and limitations of mediation as an alternative way of resolving the conflict between perpetrator and victim of domestic violence in Serbia. Starting from the premise that mediation in lighter cases of domestic violence is more preferred form of social reaction from the initiation of criminal proceedings; the authors analyse the legislation of the Republic of Serbia and point out that the mutual incompatibility of laws disables use of mediation in practice. Design/Methods/Approach: Based on acceptability of the concept of restorative justice, this scientific work analyses the advantages and limitations of mediation as an alternative way of resolving the conflict of the offender and the victim in cases of domestic violence. Authors use the comparative method, legal dogmatic method, case study method (examples for court practice in Serbia) and statistical data to examine the hypothesis that mediation may constitute a constructive way of resolving less violent conflicts within the family members and why is not enough applied in practice. Findings: Modern criminal political orientation of the “zero” tolerance of domestic violence, which was adopted in law in practice in Serbia is “blocking” use of mediation, which, in public opinion, is seen as an inadequate response to this crime. Results of the analysis show that the Serbian legislature opted for a punitive response and measures of restraining as most important mechanisms for the prevention of domestic violence. Research Limitations / Implications: These data provide insight into the marginal segment of the formal response to domestic violence in Serbia. Originality/Value: Few studies in Serbia comparing foreign experience and domestic social possibilities for the success of mediation in cases of domestic violence.
Ključne besede: alternative criminal sanction, mediation, domestic violence, Serbia
Objavljeno v DKUM: 17.04.2020; Ogledov: 1017; Prenosov: 54
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