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Record W2543424355 · doi:10.1177/1357633x16673267

Enablers and barriers in providing telediabetes services for Indigenous communities: A systematic review

2016· review· en· W2543424355 on OpenAlexaboutno aff
Sumudu Wickramasinghe, Liam J Caffery, Natalie Bradford, Anthony C Smith

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2016
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousTelehealthAcknowledgementInclusion (mineral)NursingWork (physics)Community engagementMedicineService delivery frameworkBusinessPublic relationsHealth careService (business)Medical educationTelemedicinePolitical sciencePsychologyMarketingEngineering

Abstract

fetched live from OpenAlex

A systematic review of studies which reported on telediabetes services within Indigenous communities was undertaken in June 2016. The aim of this study was to identify enablers and barriers associated with the delivery of telehealth services for diabetes care amongst Indigenous people. A total of 14 articles met the study inclusion criteria, reporting work in Canada, Australia, India, and the US. Key enablers included the use of cultural and spiritual elements, acknowledgement of local beliefs and traditions, and appropriate community engagement. The involvement of Indigenous health workers was also very important because of their role in communication in local language, helping clinicians understand the community, and the transportation of patients. The main barriers associated with telediabetes services were the potentially high fail-to-attend rates, lack of technical skills associated with the operation of telehealth equipment, and the lack of availability of local staff. Knowledge of the enablers and barriers associated with the delivery of healthcare services to Indigenous communities is important when planning a telediabetes service.

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 categoriesnone
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 score0.220
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.320
Teacher spread0.298 · 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

Citations26
Published2016
Admission routes1
Has abstractyes

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