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Record W2053749133 · doi:10.1258/135763303769211265

A study of a rural community's readiness for telehealth

2003· article· en· W2053749133 on OpenAlexafffund
Penny Jennett, Andora Jackson, Theresa Healy, Kendall Ho, Arminée Kazanjian, Robert Woollard, Susan Haydt, Joanna Bates

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2003
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity of British ColumbiaUniversity of Northern British ColumbiaUniversity of Calgary
FundersCanarie
KeywordsTelehealthFocus groupRural communityMedical educationQualitative researchPsychologyNursingTelemedicineMedicineHealth careSociologyPolitical science

Abstract

fetched live from OpenAlex

A qualitative approach was used to explore the readiness of a rural community for the implementation of telehealth services. There were four domains of interest: patient, practitioner, public and organization. Sixteen semistructured telephone interviews (three to five in each domain) were carried out with key informants and recorded on audio-tape. Two community awareness sessions were held, which were followed by five audio-taped focus groups (with five to eight people in each) in the practitioner, patient and public domains. In addition, two in-depth interviews were conducted with community physicians. Analysis of the data suggested that there were four types of community readiness: core, engagement, structural and non-readiness. The level of readiness varied across domains. There were six main themes: core readiness; structural readiness; projection of benefits; assessment of risk; awareness and education; and intra-group and inter-group dynamics. The results of the study can be used to investigate the readiness of rural and remote communities for telehealth, which should improve the chance of successful implementation.

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)0.001
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 score0.488
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.385
Teacher spread0.332 · 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

Citations114
Published2003
Admission routes2
Has abstractyes

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