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Record W2271864810 · doi:10.3928/0279-3695-20030601-11

Providers' Perceptions of How Rural Consumers Access and Use Mental Health Services

2003· article· en· W2271864810 on OpenAlexaffabout
Kimberley D Ryan-Nicholls, Frances E. Racher, Jo Robinson

Bibliographic record

VenueJournal of Psychosocial Nursing and Mental Health Services · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsBrandon University
Fundersnot available
KeywordsMental healthService providerFocus groupContext (archaeology)Qualitative researchMental health servicePerceptionBusinessPublic relationsRural healthService (business)Rural areaNursingPsychologyMarketingMedicineSociologyPolitical sciencePsychiatryGeography

Abstract

fetched live from OpenAlex

This article describes the third phase of a research study undertaken within a Canadian provincial regional health authority to explore and analyze mental health services and other resources used by rural consumers after discharge from inpatient mental health programs. The focus of this article is the qualitative research findings obtained from mental health service providers and members of allied agencies. This article will discuss the literature on rural consumers' access and use of mental health programs and services; describe the context and method used to conduct the focus groups with rural service providers; characterize access and use problems from the service providers' perspectives; and suggest strategies to address these problems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.443
Teacher spread0.417 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2003
Admission routes2
Has abstractyes

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