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Record W4417298025 · doi:10.1177/23333936251404917

An Interpretive Description of Nurses’ Perspectives on Rural Child and Youth Mental Health and Substance Use Care

2025· article· en· W4417298025 on OpenAlexaffabout
Nelly D. Oelke, Dennis Jasper, Elizabeth Keys

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2025
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsDalhousie UniversityAgriculture Food and Rural DevelopmentOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMental healthThematic analysisIntervention (counseling)ReflexivityRural areaQualitative researchPrimary careRural health

Abstract

fetched live from OpenAlex

Mental health and substance use (MHSU) disorders are the primary contributors to disability among children and youth, often with an early age of onset. Rural communities face added challenges in accessing care. Nurses may be a key professional support for rural children and youth needing MHSU care, but there is a gap in the literature about nurses' roles in this practice area. This interpretive descriptive study explored the insights of rural nurses regarding MHSU care for children and youth in community practice settings in British Columbia, Canada. Semi-structured interviews were conducted with eleven rural nurses who were either MHSU specialists or generalists with MHSU as part of their practice. Reflexive thematic analysis and interpretive description were used to analyse the data. Three key themes were constructed: (1) children and youth's mental health was tethered to the rural and remote context; (2) MHSU care was more than just MHSU treatment; and (3) the essential components of rural child and youth MHSU nursing practice. Early intervention and rural-centric approaches may support rural children and youth. By supporting rural nurses, MHSU care for children and youth can be enhanced.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.090
GPT teacher head0.489
Teacher spread0.399 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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