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Record W3047137445 · doi:10.1177/1460458220944334

An online mobile/desktop application for supporting sustainable chronic disease self-management and lifestyle change

2020· article· en· W3047137445 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

VenueHealth Informatics Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSelf-managementContext (archaeology)Quality of life (healthcare)Health careDisease managementMedicineHealth management systemThe InternetChronic diseaseComputer scienceMultimediaNursingFamily medicineWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

Health self-management has become a new trend in healthcare management due to its effectiveness in improving patient health, quality of life, and life satisfaction and simultaneously reducing the cost of care. To evaluate the potential of mobile health, we developed an online health self-management system for mobile or desktop environment to help patients self-manage their health in home settings. Certain elements (e.g. education, entertainment, and rewards) were built into the system to encourage patients to both adopt and continue using it. The system was shown to two groups of patients: an Internet-panel group of 198 patients with one or more serious chronic illnesses and 83 peripheral arterial disease patients in an in-person study group. A statistical model based on Unified Theory of Acceptance and use of Technology in a consumer context was used to analyze the results. The results from both groups confirmed that such systems, from the perspectives of patients (in a "pre-use" stage), are useful, beneficial, and rewarding to use.

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)0.003
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 score0.855
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.064
GPT teacher head0.437
Teacher spread0.373 · 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