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Record W2744810221 · doi:10.21037/mhealth.2017.07.02

Digital health and the challenge of health systems transformation

2017· article· en· W2744810221 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuemHealth · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversité LavalHôpital Saint-François d'AssiseCentres Intégré Universitaires de Santé et de Services Sociaux
FundersCanadian Institutes of Health ResearchUniversité Laval
KeywordsDigital transformationDigital healthHealth careBusinessCorporate governanceBusiness modelHealth servicesPublic relationsHealth sectorHealthcare systemData scienceKnowledge managementComputer scienceTelecommunicationsMarketingPolitical scienceWorld Wide WebEconomic growthEconomicsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Information and communication technologies have transformed all sectors of society. The health sector is no exception to this trend. In light of "digital health", we see multiplying numbers of web platforms and mobile health applications, often brought by new unconventional players who produce and offer services in non-linear and non-hierarchal ways, this by multiplying access points to services for people. Some speak of a "uberization" of healthcare. New realities and challenges have emerged from this paradigm, which question the abilities of health systems to cope with new business and economic models, governance of data and regulation. Countries must provide adequate responses so that digital health, based increasingly on disruptive technologies, can benefit for all.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.000
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 head0.084
GPT teacher head0.440
Teacher spread0.356 · 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