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Record W2146761117 · doi:10.3402/ijch.v70i5.17859

Innovations in health service organization and delivery in northern rural and remote regions: a review of the literature

2011· review· en· W2146761117 on OpenAlexaff
Craig Mitton, François Dionne, Lisa Masucci, Sabrina T. Wong, Susan Law

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2011
Typereview
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsSt Mary's Hospital CentreVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsTelehealthCINAHLWorkforceeHealthPublic healthHealth careMEDLINEBusinessNursingTelemedicineRural areaRural healthMedicineKnowledge managementPublic relationsPolitical sciencePsychological interventionComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify and review innovations relevant to improving access, quality, efficiency and/or effectiveness in the organization and delivery of health care services in rural and remote areas. STUDY DESIGN: Literature review. METHODS: Key bibliographic databases that index health research were searched: MEDLINE, EMBASE and CINAHL. Other databases relevant to Arctic health were also accessed. Abstracts were assessed for relevancy and full articles were reviewed and categorized according to emergent themes. RESULTS: Many innovations in delivering services to rural and remote areas were identified, particularly in the public health realm. These innovations were grouped into 4 key themes: organizational structure of health services; utilization of telehealth and ehealth; medical transportation; and public health challenges. CONCLUSIONS: Despite the challenges facing rural and remote regions, there is a distinctly positive message from this broad literature review. Evidence-based initiatives exist across a range of areas - which include operational efficiency and integration, access to care, organizational structure, public health, continuing education and workforce composition - that have the potential to positively impact health care quality and health-related outcomes.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.397
Teacher spread0.347 · 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 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

Citations102
Published2011
Admission routes1
Has abstractyes

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