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Record W3164990747 · doi:10.1177/14604582211020064

Rural use of health service and telemedicine during COVID-19: The role of access and eHealth literacy

2021· article· en· W3164990747 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 · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsTelemedicineeHealthPandemicLiteracyCoronavirus disease 2019 (COVID-19)MedicineHealth literacyPhoneHealth careTelehealthRural areaNursingFamily medicinePsychologyPolitical science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has driven a greater reliance on telemedicine, yet rural access, use, and satisfaction with telemedicine and the role of eHealth literacy are unknown. Using a cross-sectional design, 279 (70.6% female) western rural Canadians completed an online survey. The majority of participants reported access to telemedicine, but nearly 1/5 lacked access to online or virtual mental health services. The majority of participants had used health care services following the declared COVID-19 pandemic in North America, and just under half had used telemedicine. Telemedicine satisfaction scores were higher among participants who had used video ( M = 4.18) compared to those who used phone alone ( M = 3.79) ( p = 0.031). Telemedicine satisfaction and eHealth literacy were correlated ( r = 0.26, p = 0.005). Participants did not want telemedicine to replace in-person consultations. Telemedicine practice requires that rural residents have the resources, ability and willingness to engage with remote 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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.595

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 studies0.0010.000
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 head0.068
GPT teacher head0.418
Teacher spread0.350 · 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