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Record W2969059210

The landscape of mental health services in rural Canada

2019· article· en· W2969059210 on OpenAlexvenueaboutno aff
Erik Loewen Friesen

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthSociocultural evolutionRural areaHealth careRural healthMedicineMental illnessNursingPsychiatryEconomic growthPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Many Canadians living in rural and remote communities face difficulty accessing mental health services. This has become a pressing issue in the Canadian healthcare system due to an increased focus on mental health and the high rate of suicide in rural regions as compared to urban communities. The inaccessibility of mental health services in rural Canada can only be partially explained by the lack of psychiatrists working in these areas. Additional access barriers arise from sociocultural nuances within individual rural communities, including an increased value placed on self-reliance and stigmatization of seeking mental health support. It has been challenging for mental health services to adequately address the vast social, economic and cultural differences that exist among individual rural communities – a reality that necessitates holistic mental health programs tailored to the unique complexities of each community. Nonetheless, programs aiming to improve accessibility of rural mental health services do exist across Canada, often employing technology to deliver psychiatric support to rural patients or provide guidance to rural primary care physicians who care for patients with mental illness. This narrative review outlines the barriers that are impeding mental health care in rural Canada, the existing strategies to circumvent these barriers, and the role of current medical students in the future of rural psychiatry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.007
GPT teacher head0.305
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2019
Admission routes2
Has abstractyes

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