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M-Health: Can Chronic Obstructive Pulmonary Disease Patients Use Mobile Phones and Associated Software to Self-Manage Their Disease?

2012· article· en· W176403260 on OpenAlexaffabout
Elizabeth M. Borycki

Bibliographic record

VenueStudies in health technology and informatics · 2012
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPulmonary diseaseDiseaseMedicineChronic diseaseSoftwareComputer scienceIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in Canada. The Internet and mobile phones have revolutionized the ways in which those affected by chronic illnesses obtain health information. Increasingly individuals affected by chronic illnesses are using social media (e.g., blogs, YouTube®, Facebook®, Twitter®) to obtain information about and social support for their conditions. This rate is expected to grow with the increased use of the Internet, mobile phones, and mobile phone/social media software applications. The future of COPD self-management is changing and will likely include the use of a blend of these three technologies. A literature review was undertaken to determine the current state of the research at the intersection of COPD and the use of mobile devices and mobile social media applications in health care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.036
GPT teacher head0.378
Teacher spread0.342 · 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.

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

Citations13
Published2012
Admission routes2
Has abstractyes

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