Space, place, and the politics of access: Service provider perspectives on health system responses to sexual- and gender-based violence in rural communities
Bibliographic record
Abstract
BACKGROUND: Sexual- and gender-based violence (SGBV) remains a pervasive public health concern in Canada, with rural communities facing disproportionate rates of severe violence alongside significant barriers to care. While previous research has documented rural service scarcity, less attention has been paid to the socio-spatial dynamics that shape how health and support services are experienced, accessed, and provided. METHODS: This qualitative study draws on semi-structured interviews with 18 SGBV service providers working in rural Ontario. Guided by an intersectional geography framework, we examine how providers interpret and navigate the spatial, structural, and sociocultural conditions influencing care, particularly for groups experiencing intersecting forms of marginalization. Reflexive thematic analysis was used to identify key themes across institutional healthcare settings and community-based supports. FINDINGS: Two overarching themes emerged: (1) SGBV care pathways in a rural context: Fragmentation, stigma, and scope of support, which highlights how institutionally-constrained systems (i.e., emergency and primary care) often operate on crisis-driven thresholds that may not be suited to meet survivors where they are; and (2) Reimagining rural SGBV response through multi-service hubs, which explores integrated, place-based care models as promising but underfunded alternatives. Across interviews, participants emphasized that access is shaped not only by geographic distance and logistical specificities, but by safety, stigma, trust, and relational continuity. CONCLUSION: This study illustrates how spatial and institutional inequities intersect to limit meaningful access to SGBV-related healthcare in rural contexts. Findings support the development of community-embedded models that respond to the place-based realities of survivors and providers alike, such as rural care hubs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".