Healthcare Access Challenges and Facilitators for Back Pain Across the Rural-Urban Continuum in Saskatchewan, Canada: Cross-Sectional Results From a Provincial-Wide Telephone Survey
Bibliographic record
Abstract
Background: Chronic back pain is a common musculoskeletal disorder, disproportionately affecting rural and Indigenous people. Saskatchewan has a relatively high proportion of rural and Indigenous residents; therefore, understanding barriers and facilitators to accessing healthcare are needed to improve healthcare service delivery. Methods: A provincial-wide telephone survey explored experiences and perceived healthcare access barriers and facilitators among 384 Saskatchewan residents who experienced chronic low back pain. Chi-squared tests were performed to determine if people who lived in urban versus rural areas differed in the proportion who had accessed services from various healthcare practitioners. T-test and Mann-Whitney U analyses were conducted to determine differences between urban and rural, and Indigenous and non-Indigenous respondents. Results: Of 384 residents surveyed, 234 (60.9%) reported living in a rural location; 21 (5.5%) identified as Indigenous. Wait times (47%), cost (40%), travel (39%), and not knowing how to seek help (37%) were the most common barriers for Saskatchewan residents seeking care, with travel being the only barrier that was significantly different between rural and urban respondents ( P ⩽ .001). Not knowing where to go to access care or what would help their low back pain ( P = .03), lack of cultural sensitivity ( P = .007), and comfort discussing problems with health care professionals ( P = .26) were greater barriers for Indigenous than non-Indigenous participants. Top facilitators (>50% of respondents) included publicly funded healthcare, locally accessible healthcare services, and having supportive healthcare providers who facilitate referral to appropriate care, with urban respondents considering the latter 2 as greater facilitators than rural respondents. Telehealth or virtual care ( P = .013) and having healthcare options nearby in their community ( P = .045) were greater facilitators among Indigenous participants compared to non-Indigenous respondents. Conclusions: Rural, urban, Indigenous, and non-Indigenous people report overlapping and unique barriers and facilitators to accessing care for chronic low back pain. Understanding perceived access experiences will assist in developing more effective care models for specific communities or regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".