Navigating fragmented services: a gender-based violence (GBV) critical feminist analysis of women’s experiences engaging with health and social supports in three Canadian cities
Bibliographic record
Abstract
Abstract Background Gender-based violence (GBV) remains a pervasive public health crisis with devastating impacts on women’s health and well-being. Women experiencing GBV face considerable barriers accessing appropriate and timely health and social services. This study explored women’s experiences with health and social services in three Canadian cities to understand critical challenges and strengths in service provision for women experiencing GBV. Methods In-depth interviews were conducted with self-identifying women (n = 21) who had accessed health or social care services and with service providers (n = 25) in three Canadian cities between February 2021 and November 2022. Women’s interviews focused on experiences engaging with services including what worked well, the challenges they faced, and their recommendations to enhance service delivery to women experiencing violence. Staff interviews focused on their experiences of providing services within their organization, and the strengths and challenges in providing services to women within their community. Data were analyzed using reflexive thematic analysis with a gender-based violence critical feminist lens. Results We organized the findings into three interrelated themes. First our results show how the systems within which health and social services are organized, are not designed to meet women’s complex needs, with rigid structures, siloed services, and stigmatizing cultures creating significant barriers. Second, the data illustrate how service providers support and empower women through practices such as providing key information, assisting with administrative tasks, offering material resources, and addressing discrimination through advocacy and accompaniment. Third, our findings demonstrate how building an effective working relationship characterized by trust, non-judgment, and collaboration is crucial for service engagement and women’s overall well-being. Conclusions Findings illuminate critical public health challenges as women navigate fragmented services across multiple and siloed systems not designed to meet their complex needs. There is an urgent need for systemic change to create more integrated, responsive support systems for women experiencing GBV. This includes addressing underlying structures perpetuating gender inequities and violence. Facilitating safe access to holistic services that consider women’s preferences is crucial. Effective working relationships built on trust, respect, and power-sharing are key to supporting women’s agency and addressing their interconnected needs.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.067 | 0.025 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".