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Record W4392121764 · doi:10.1017/s0714980824000102

Virtual care during COVID-19: The perspectives of older adults and their healthcare providers in a cardiac rehabilitation setting

2024· article· en· W4392121764 on OpenAlexaff
Cecilia Flores‐Sandoval, Shannon L. Sibbald, Bridget Ryan, Tracey L. Adams, Neville Suskin, Robert S. McKelvie, Jacobi Elliott, J. B. Orange

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteSt. Joseph's HospitalCentre for Family MedicineWestern University
Fundersnot available
KeywordsRehabilitationHealth careCoronavirus disease 2019 (COVID-19)Exploratory researchNursingQualitative researchPsychologyMedicineDiseasePhysical therapy

Abstract

fetched live from OpenAlex

The present study aimed to explore the perspectives of older adults and health providers on cardiac rehabilitation care provided virtually during COVID-19. A qualitative exploratory methodology was used. Semi-structured interviews were conducted with 15 older adults and 6 healthcare providers. Five themes emerged from the data: (1) Lack of emotional intimacy when receiving virtual care, (2) Inadequacy of virtual platforms, (3) Saving time with virtual care, (4) Virtual care facilitated accessibility, and (5) Loss of connections with patients and colleagues. Given that virtual care continues to be implemented, and in some instances touted as an optimal option for the delivery of cardiac rehabilitation, it is critical to address the needs of older adults living with cardiovascular disease and their healthcare providers. This is particularly crucial related to issues accessing and using technology, as well as older adults' need to build trust and emotional connection with their providers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0050.004
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.269
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2024
Admission routes1
Has abstractyes

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