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Record W4389102331 · doi:10.12927/hcq.2023.27217

Was Virtual Care as Safe as In-Person Care? Analyzing Patient Outcomes at Seven and Thirty Days in Ontario during the COVID-19 Pandemic

2023· article· en· W4389102331 on OpenAlexaffvenueabout
Shawn Mondoux, Frank Battaglia, Anastasia Gayowsky, Natasha Clayton, Caillin Langmann, Paul D. Miller, Alim Pardhan, Julie Mathews, Alexander Drossos, Keerat Grewal

Bibliographic record

VenueHealthcare Quarterly · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSchwartz/Reisman Emergency Medicine InstituteMcMaster UniversityHamilton Health SciencesSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Acute careBest practiceNursingMedical emergencyHealth careFamily medicinePolitical science

Abstract

fetched live from OpenAlex

In 2020, almost overnight, the paradigm for healthcare interactions changed in Ontario. To limit person-to-person transmission of COVID-19, the norm of in-person interactions shifted to virtual care. While this shift was part of broader public health measures and an acknowledgment of patient and societal concerns, it also represented a change in care modalities that had the potential to affect the quality of care provided, as well as short- and long-term patient outcomes. While public policy decisions were being made to moderate the use of virtual care at the end of the declared pandemic, a thorough analysis of short-term patient outcomes was needed to quantify the impact of virtual care on the population of Ontario.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.002
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
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 head0.046
GPT teacher head0.357
Teacher spread0.312 · 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 designObservational
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

Citations7
Published2023
Admission routes3
Has abstractyes

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