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Record W1585119766 · doi:10.17705/1jais.00263

The Clinical Impact of eHealth on the Self-Management of Diabetes: A Double Adoption Perspective

2011· article· en· W1585119766 on OpenAlexaff
Helen Kelley, Mike Chiasson, Angela Downey, Danièle Pacaud

Bibliographic record

VenueJournal of the Association for Information Systems · 2011
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of CalgaryUniversity of VictoriaUniversity of Lethbridge
Fundersnot available
KeywordseHealthPerspective (graphical)Promotion (chess)Health careKnowledge managementMedicineTelemedicineHealth promotionmHealthNursingPsychologyComputer sciencePsychological interventionPublic healthPolitical science

Abstract

fetched live from OpenAlex

The development, adoption, and acceptance of eHealth systems that change and improve patient self-care have been promising, but the results have been mixed and the work mostly atheoretical. In this paper, we respond to this opportunity by developing and assessing an eHealth system for newly diagnosed type 2 diabetes patients. Study participants used the eHealth system for a 12-month period after diagnosis in an attempt to acquire an understanding about their diabetes, develop self-care activities (e.g., blood glucose testing), and improve their biomedical outcomes. Drawing upon theories and methods from information systems and upon the Precede-Proceed model of health promotion planning, we explored the double adoption of eHealth technology and its antecedents, self-care practices and their antecedents, and improvements in biomedical outcomes important to long-term diabetes health. Path model results indicate important implications for information systems, eHealth, and health promotion practice and research, which are discussed.

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)0.012
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.086
GPT teacher head0.445
Teacher spread0.358 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations43
Published2011
Admission routes1
Has abstractyes

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