P.191 Factors affecting access to neurosurgical care in diverse communities in Canada: a qualitative scoping review
Bibliographic record
Abstract
Background: Access to neurosurgical care is vital for conditions such as traumatic brain injuries and brain tumours. However, significant disparities in healthcare access persist in Canada, disproportionately affecting rural, Indigenous, and socioeconomically disadvantaged populations. This qualitative scoping review examines barriers and facilitators to neurosurgical access, addressing gaps in the literature concerning equity-deserving groups. Methods: A systematic literature search (2000–2024) was conducted within MEDLINE, EMBASE, Cochrane Library, PsycINFO, and Scopus, along with gray literature from governmental and non-governmental organizations. From 1400 identified records, eight qualitative or mixed-methods studies met the inclusion criteria. Thematic analysis was conducted to explore socioeconomic, geographic, racial, gender-based, and cultural barriers. Results: Four major themes emerged: delays in access, alternative healthcare options, policy barriers, and communication and coordination issues. Barriers such as transportation gaps, socioeconomic inequities, and systemic discrimination were particularly pronounced for rural and Indigenous populations. Facilitators like telehealth and improved inter-hospital coordination show potential but are limited by infrastructure constraints and cultural misalignments. Conclusions: Addressing barriers to neurosurgical care requires systemic reforms, including equitable resource allocation, expanded digital infrastructure, and culturally competent care. The lack of intersectional research on overlapping barriers underscores the need for future studies to prioritize tailored interventions to ensure timely, equitable neurosurgical care across Canada.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".