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Record W2183468993 · doi:10.3233/978-1-58603-979-0-472

Innovation in the North: Are Health Service Providers Ready for the Uptake of an Internet-based Chronic Disease Management Platform?

2009· article· en· W2183468993 on OpenAlexaffabout
Sherri M. Tillotson, Scott A. Lear, Yuriko Araki, Dan Horvat, Ken Prkachin, Joanna Bates, Ellen Balka

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsThe InternetBusinessDiseaseService providerInternet privacyInternet service providerChronic diseaseService (business)World Wide WebKnowledge managementMedicineTelecommunicationsComputer scienceMarketingFamily medicinePathology

Abstract

fetched live from OpenAlex

Remote and rural regions in Canada are faced with unique challenges in the delivery of primary health services. The purpose of this study was to understand how patients and healthcare professionals in northern British Columbia might make use of the Internet to manage cardiovascular diseases. The study used a qualitative methodology. Eighteen health professionals and 6 patients were recruited for a semi-structured interview that explored their experience in managing patients with cardiovascular disease and their opinions and preferences about the use of the Internet in chronic disease management. Key findings from the data suggest that a) use of the Internet helps to maintain continuity of care while a patient moves through various stages of care, b) the Internet may possibly be used as an educational tool in chronic disease self-management, c) there is a need for policy development to support Internet-based consultation processes, and d) while health providers endorse the notion of electronic advancement in their practice, the need for secure and stable electronic systems is essential.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.080
GPT teacher head0.345
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations1
Published2009
Admission routes2
Has abstractyes

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