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Record W4404560052 · doi:10.22605/rrh9106

A scoping review of rural mental health and substance use nursing

2024· review· en· W4404560052 on OpenAlexaffabout
Stuart Thomas, Nelly D. Oelke, Dennis Jasper, Michelle Pavloff, Elizabeth Keys

Bibliographic record

VenueRural and Remote Health · 2024
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of CalgaryDalhousie UniversitySaskatchewan PolytechnicUniversity of British Columbia, Okanagan CampusInterior Health
Fundersnot available
KeywordsSubstance useMental healthNursingMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Globally, nearly 50% of the population live in rural areas, while just 36% of nurses serve in these locations. Rural nurses face distinct challenges such as limited resources and geographical isolation, and often work with an expanded scope of practice that includes mental health and substance use (MHSU) care. The extent to which rural nurses engage in MHSU care, care barriers, and facilitators has not been previously well described. Thus, this scoping review explored the international research on rural MHSU nursing. The aim was to synthesize the rural MHSU nursing evidence and consider it in relation to Knowing the Rural Community: A Framework for Nursing Practice in Rural and Remote Canada. The research question for this review was, 'What is known about rural nursing related to mental health and/or substance use considerations?' METHODS: A scoping review approach was used to guide a systematic exploration of the literature. CINAHL, Medline, and PsycINFO databases were searched for international qualitative, quantitative, and mixed-methods scholarly articles with rural MHSU nursing considerations, with no date limiters. Extracted data were mapped to the framework's categories: rural people, community, rural context, and larger society. RESULTS: Forty-seven articles were selected for this critical review of the literature, with most of the articles from Australia (n=15), the US (n=8), Canada (n=7), and South Africa (n=5), and representing rural nurses who worked in hospital (n=16), primary care (n=11), community mental health (n=7), and emergency department (n=6) practice settings. Rural MHSU nursing was described as a generalist and multifaceted role, with challenges such as workplace violence, practice setting and community isolation, and resource inadequacies. Results also indicated that rural MHSU nursing is influenced by a nurse's preparedness for their role, with a lack of preparedness complicated by multilayered resource deficits. Social determinants of health, mental health stigma, and health inequities also affected rural MHSU nursing practice. Despite facing significant barriers, rural nurses demonstrated resilience and commitment to providing quality MHSU care for their communities. DISCUSSION: Overall, there was congruence between the included studies and the framework. The framework provided a comprehensive foundation for this scoping review. However, based on the findings of this scoping review, minor amendments to the framework are recommended, such as including the rural nurse as an explicit part of the framework. Further, a rural-centric approach that is local, context-sensitive, and developed in collaboration with rural people, was identified as crucial for addressing the unique challenges faced by rural MHSU nurses and their communities. Future rural research should address nursing shortages, practice support, and under-researched areas such as child and youth MHSU nursing and Indigenous health. CONCLUSION: This scoping review highlighted some of the challenges rural MHSU nurses encounter and provided valuable insights into the complexities of rural MHSU nursing internationally. By using the framework to organize and synthesize the literature, this study contributed to a deeper understanding of the role of rural nurses in addressing MHSU challenges and the context in which rural MHSU nursing care may be situated.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.126
GPT teacher head0.524
Teacher spread0.398 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes2
Has abstractyes

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