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Record W2015462694 · doi:10.1089/153056204773644625

Stakeholder Readiness for Telehomecare: Implications for Implementation

2004· article· en· W2015462694 on OpenAlexaffabout
Marilynne Hebert, Barbara Korabek

Bibliographic record

VenueTelemedicine Journal and e-Health · 2004
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTelehealthStakeholderNursingFocus groupHealth careBusinessQualitative researchIntervention (counseling)Quality (philosophy)MedicineKnowledge managementTelemedicinePublic relationsMarketingComputer science

Abstract

fetched live from OpenAlex

Numerous pilot studies have demonstrated that telehomecare technology may improve client outcomes through timely intervention and health crises prevention, thereby reducing return visits to hospitals and physician offices. Although the potential of telehomecare to increase access to services and improve quality of care and health outcomes is recognized, expectations for its widespread adoption have not been realized. Factors affecting diffusion of innovations include, among other things, perceptions of the technology, organizational characteristics, and communication. These require further exploration for telehealth applications because evidence alone will not automatically produce large-scale conversions in practice. This 12-month study was designed to assess the readiness of clients, health care professionals, and organizations to adopt telehomecare services for adult diabetic clients within the Calgary Health Region. A qualitative approach was used to collect data through focus groups with clients and home care nurses along with interviews with family physicians and key informants responsible for planning and resource allocation in diabetic homecare and telehealth programs. The transcripts of these interviews were analyzed for themes, which were categorized with respect to their effect on quality of care (including structure, process or outcome of care), including those related to the individual client, the health care provider, and the organization as a whole. The study findings identified differences in stakeholder conceptions of the technology, including common themes among clients, providers, and organizations. Implications of study results for developing a strategy to incorporate telehomecare into routine community care 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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.152
GPT teacher head0.448
Teacher spread0.296 · 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 designOther design
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

Citations45
Published2004
Admission routes2
Has abstractyes

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