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Implementation barriers for mHealth for non-communicable diseases management in low and middle income countries: a scoping review and field-based views from implementers

2020· review· en· W3000110824 on OpenAlexaff
Josefien van Olmen, Erica Erwin, Ana Cristina García-Ulloa, Bruno Meessen, J. Jaime Miranda, Kirsty Bobrow, Juliet Iwelunmore, Ucheoma Nwaozuru, Chisom Obiezu Umeh, Carter Smith, Chris Harding, Pratap Kumar, Clicerio Gonzales, Sergio Hernández-Jiménez, Karen Yeates

Bibliographic record

VenueWellcome Open Research · 2020
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsQueen's University
FundersNational Institute of Neurological Disorders and StrokeNational Health and Medical Research CouncilDepartment for International Development, UK GovernmentEconomic and Social Research CouncilChinese Academy of Medical SciencesFogarty International CenterDepartment for International DevelopmentMedical Research CouncilWellcomeSouth African Medical Research CouncilConsejo Nacional de Ciencia y TecnologíaWellcome TrustNational Institutes of HealthUnited States Agency for International Development
KeywordsmHealthPsychological interventionContext (archaeology)Non-communicable diseaseBusinessKnowledge managementMedicineComputer scienceNursingGeographyPublic health

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.180
metaresearch head score (Gemma)0.320
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.180
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1800.320
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0140.016
Science and technology studies0.0030.003
Scholarly communication0.0120.012
Open science0.0030.006
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.333
GPT teacher head0.612
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations53
Published2020
Admission routes1
Has abstractyes

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