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Record W2896657308 · doi:10.9745/ghsp-d-18-00230

Establishing Standards to Evaluate the Impact of Integrating Digital Health into Health Systems

2018· article· en· W2896657308 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Health Science and Practice · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsImpact
FundersNational Institutes of HealthNational Center for Advancing Translational SciencesJohns Hopkins UniversityWorld Health OrganizationAetna Foundation
KeywordsDigital healthSustainabilityBridge (graph theory)Context (archaeology)Scale (ratio)Key (lock)Activity-based costingData sciencePolitical sciencePublic relationsComputer scienceKnowledge managementHealth careManagement scienceEngineering ethicsBusinessEngineeringMedicineComputer securityMarketingGeography

Abstract

fetched live from OpenAlex

The key milestones in the rise of digital health illustrate efforts to bridge gaps in the evidence base, a shifting focus to scale-up and sustainability, growing attention to the precise costing of these strategies, and an emergent implementation science agenda that better characterizes the ecosystem-the social, political, economic, legal, and ethical context that supports digital health implementation-necessary to take digital health approaches to scale.

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.

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)0.047
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0080.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.072
GPT teacher head0.576
Teacher spread0.504 · 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