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Record W4409251341 · doi:10.1055/s-0044-1800717

Precision and Virtual Care

2024· article· en· W4409251341 on OpenAlex
Elizabeth M. Borycki, Femke van Sinderen, Linda Peute, Sasha A. Zinovich, David R. Kaufman, Vivian Vimarlund, André Kushniruk

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

VenueYearbook of Medical Informatics · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceContext (archaeology)Virtual patientVirtual machineEmerging technologiesSoftwareHuman–computer interactionRisk analysis (engineering)MedicineNursingArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

The importance of virtual care has been highlighted by the recent pandemic which emphasized the need for effectively providing care remotely. In addition, the development of a range of emerging technologies to support virtual care has accelerated this trend. Technologies may vary in complexity from low (e.g., technologies that can be used easily by patients) to high (e.g., use of sophisticated software and hardware to support virtual care). In this article virtual care is first defined, followed by a discussion of a range of virtual care technologies. A framework is then described that can be used to consider and reason about virtual care in terms of both technology complexity as well as patient complexity. Examples of virtual care that can be considered using the framework are provided. It is argued that achieving an appropriate fit between the level of complexity of the technology involved and patient context will lead to improved care and ultimately precision virtual care. Implications of the approach presented are explored.

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: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.366
Teacher spread0.344 · 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