The landscape of non-psychotic psychiatric illness in rural Canada: a narrative review
Bibliographic record
Abstract
INTRODUCTION: Canada's rural population has diverse demographic features and accounts for 18.9% of Canada's population. Indigenous Peoples (First Nations, Inuit, and Métis), who are highly represented in rural communities, have additional risk factors related to colonialism, and historical and ongoing trauma. Understanding how to best respond to elevated rates of psychiatric illness in rural and remote communities requires an understanding of the unique challenges these communities face in accessing and providing high quality psychiatric services. This article reports a review of published literature on prevalence of non-psychotic psychiatric conditions, as well as the risk and protective factors influencing rates and experience of mental illness in rural and remote communities in Canada to help inform approaches to prevention and treatment. METHODS: This focused narrative review of literature related to rural non-psychotic psychiatric illness in rural and remote Canada published over a 20-year period (October 2001 - February 2023). A review of CINAHL, Medline and Academic Search Complete databases supplemented by gray literature (eg federal and provincial documents, position papers, and clinical practice guidelines) identified by checking reference lists of identified articles, and web searches. A textual narrative approach was used to describe the literature included in the final data set. RESULTS: A total of 32 articles and 13 gray literature documents were identified. Findings were organized and described in relation to depression and anxiety and substance use suicidality and loss; rates for all were noted as elevated in rural communities. Different mental health strategies and approaches were described. Variability in degree of rurality, or proximity to larger metropolitan centers, and different community factors including cohesiveness and industrial basis, were noted to impact mental health risk and highlighted the need for enhancing family physician capacity and responsiveness and innovative community-based interventions, in addition to telepsychiatry. CONCLUSION: Further focus on representative community-based research is critical to expand our knowledge. It is also critical to consider strategies to increase psychiatric care access, including postgraduate medical training and telehealth training.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".