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Record W7162668317 · doi:10.17605/osf.io/vjz2w

Mapping the Landscape of Rural and Remote Health Services: Review of Review

2025· article· W7162668317 on OpenAlexaffabout
Abhisha M. Rathod, Femke Hoekstra

Bibliographic record

VenueOpen Science Framework · 2025
Typearticle
Language
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHealth careWorkforceService delivery frameworkRural areaWork (physics)Service (business)TelemedicineService provider

Abstract

fetched live from OpenAlex

Rural and remote communities in Canada, and globally, face long-standing disparities in healthcare access, delivery, and outcomes. Geographic isolation, insufficient infrastructure, provider shortages, and socio-economic challenges create systemic barriers that compromise the quality and continuity of care for rural populations. In many rural and remote regions, persistent healthcare workforce shortages, frequent service disruptions, and limited access to emergency care underscore critical gaps in policy and service delivery. At the same time, emerging service models such as virtual care, mobile clinics, and integrated care pathways offer new avenues to address these persistent inequities. However, there remains a lack of systematic evidence to determine which innovations work best, for whom, and under what circumstances. This review of reviews aims to systematically identify and synthesize the peer-reviewed literature on rural and remote health services. The objectives are to: 1. Summarize the current state of healthcare services and healthcare services research in rural and remote settings. 2. Identify key themes, barriers, enablers, and service delivery innovations. 3. Identify research gaps and support the development of policy recommendations which will be used to inform the development of the research agenda in British Columbia.

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)0.024
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Insufficient payload (model declined to judge)
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 score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.009
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0060.004
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.469
Teacher spread0.406 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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