The subjective experience of trauma within structural marginality: An ethnography of mental health among survivors of gender-based violence in Italy
Bibliographic record
Abstract
BACKGROUND: Gender-based violence (GBV) has multi-dimensional impacts on women's mental health and everyday life, often leading to experiences of trauma, PTSD and co-morbid mental health conditions. Institutional practices and strategies designed to support survivors of gender-based violence can collide with, overshadow and misapprehend women's own subjective experiences. AIMS: This study aims to highlight the importance of subjective accounts in understanding women's mental health and the complexity of trauma experienced by female survivors of gender-based violence. It aims to put forward survivors' voices that are often excluded from research. METHOD: This study draws on 12 months of ethnographic fieldwork (2021-2022) among 38 women seeking psychological support from anti-violence centres in Milan, Italy. The participants, including 12 migrant women, represented a diverse set of demographic backgrounds. The study also incorporated accounts from mental health professionals and NGO coordinators. Using anthropological and phenomenological approaches, this qualitative analysis is based on themes which illustrate survivors' lived experiences of trauma. RESULTS: This ethnography identified several pathways contributing to poor mental health among GBV survivors. Key findings reveal that survivors engage with trauma and mental health symptoms through subjective, embodied and temporally informed processes, affecting their recovery and societal vulnerability. Structural marginalisation, including insufficient government funding, prolonged legal procedures and invasive medical practices, further impacted survivors' well-being. Themes from the analysis demonstrated that structural marginality and isolation exacerbated mental health issues, hindering empowerment and autonomy; thus trapping survivors in a state of long-term vulnerability. CONCLUSION: This study emphasises the importance of incorporating subjective accounts to understand women's mental health in-depth. The findings highlight that current services often fail to address the complexity of trauma, leading to inadequate support and prolonged marginalisation. To improve outcomes, it is crucial to offer tailored mental health support, address socio-economic challenges and implement trauma-informed care that fosters safety, empowerment and resilience.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".