Health disparities among rural individuals with mental health conditions: A systematic literature review.
Bibliographic record
Abstract
There is growing concern about the availability of healthcare services for rural patients. This systematic literature review evaluates original research on health disparities among rural and urban populations with mental health conditions in North America. Using PRISMA guidelines, we used four electronic databases (Pubmed, Cochrane, PsychInfo, Web of Science) and hand searches and included original research conducted in the United States or Canada before July 2021 that compared health outcomes of patients with any mental health disorder in rural versus non-rural areas. Both qualitative and quantitative data were extracted including demographics, mental health condition, health disparity measure, rural definition, health outcome measures/main findings, and delivery method. To evaluate study quality, the modified Newcastle Ottawa Scale was used. Our initial search returned 491 studies and 17 studies met final inclusion criteria. Mental health disorders included schizophrenia (4 studies), PTSD (10), mood disorders (9), and anxiety disorders (6). Total sample size was 5,314,818 with the majority being military veterans. Six studies (35.2%) showed no significant rural-urban disparities while eleven (64.7%) identified at least one. Of those, nine reported worse outcomes for rural patients. The most common disparities were diagnostic differences, increased suicide rates and access problems. This review found mixed results regarding outcomes in rural patients with mental health disorders. Disparities were found regarding risk of suicide and access to services. Telehealth in addition to in person outreach to these rural communities may be alternatives to impact these outcomes.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".