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Record W3042887736 · doi:10.1177/0840470420938818

Virtual care: Enhancing access or harming care?

2020· article· en· W3042887736 on OpenAlex
Lorian Hardcastle, Ubaka Ogbogu

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsHealth careQuality (philosophy)Internet privacyNursingPrimary careMedical careBusinessMedicineComputer scienceFamily medicinePolitical science

Abstract

fetched live from OpenAlex

COVID-19 has catalyzed the adoption of virtual medical care in Canada. Virtual care can improve access to healthcare services, particularly for those in remote locations or with health conditions that make seeing a doctor in person difficult or unsafe. However, virtual walk-in clinic models that do not connect patients with their own doctors can lead to fragmented, lower quality care. Although virtual walk-in clinics can be helpful for those who temporarily lack access to a family doctor, they should not be relied on as a long-term substitute to an established relationship with a primary care provider. Virtual care also raises significant privacy issues that policy-makers must address prior to implementing these models. Patients should be cautious of the artificial intelligence recommendations generated by some virtual care applications, which have been linked to quality of care concerns.

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)0.000
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 score0.859
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.058
GPT teacher head0.388
Teacher spread0.330 · 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