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Record W4387824529 · doi:10.1016/j.cjco.2023.10.013

“Comfort of Sitting at Home While Getting Information I Needed”: Experiences of Cardiac Patients Attending Virtual Cardiac Rehabilitation

2023· article· en· W4387824529 on OpenAlexaff
Matthew R. Fuda, Pooja Patel, Judy Van Es, Karen Mosleh, Katelyn J Cullen, Eva Lonn, Jon-David Schwalm, Jacob Crawshaw

Bibliographic record

VenueCJC Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsRehabilitationSittingVirtual patientCoronavirus disease 2019 (COVID-19)MedicineHealthcare deliveryPandemicMedical emergencyHealth carePhysical therapyNursingInternal medicineDisease

Abstract

fetched live from OpenAlex

Because of the COVID-19 pandemic, several health care services, including cardiac rehabilitation (CR), had to transition to virtual delivery, for which formal evaluations are lacking. In this pilot study, we investigated the implementation of a virtual CR program by surveying 30 patients attending virtual CR. Virtual CR was well received, although patients provided recommendations to improve delivery such as offering individual sessions and changing how education materials were delivered. Virtual delivery of CR likely has a role in health care, either independently or as part of a hybrid model; however, further evaluation is required.

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)0.003
metaresearch head score (Gemma)0.011
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 score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0050.003
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.326
Teacher spread0.308 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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