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Record W4315705177 · doi:10.2196/40267

Characteristics and Health Care Use of Patients Attending Virtual Walk-in Clinics in Ontario, Canada: Cross-sectional Analysis

2023· article· en· W4315705177 on OpenAlexafffundabout
Lauren Lapointe‐Shaw, Christine Salahub, Cherryl Bird, R. Sacha Bhatia, Laura Desveaux, Richard H. Glazier, Lindsay Hedden, Noah Ivers, Danielle Martin, Yingbo Na, Sheryl Spithoff, Mina Tadrous, Tara Kiran

Bibliographic record

VenueJournal of Medical Internet Research · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSimon Fraser UniversitySt. Michael's HospitalSinai Health SystemTrillium Health CentreWomen's College HospitalUniversity of TorontoUniversity Health Network
FundersToronto General Hospital Research Institute, University Health NetworkCanadian Institutes of Health ResearchUniversity of TorontoWomen's College Hospital
KeywordsCross-sectional studyWalk-inMedicineFamily medicineHealth careEmergency departmentPandemicPopulationCoronavirus disease 2019 (COVID-19)NursingAlternative medicineDiseaseEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Funding changes in response to the COVID-19 pandemic supported the growth of direct-to-consumer virtual walk-in clinics in several countries. Little is known about patients who attend virtual walk-in clinics or how these clinics contribute to care continuity and subsequent health care use. OBJECTIVE: The objective of the present study was to describe the characteristics and measure the health care use of patients who attended virtual walk-in clinics compared to the general population and a subset that received any virtual family physician visit. METHODS: This was a retrospective, cross-sectional study in Ontario, Canada. Patients who had received a family physician visit at 1 of 13 selected virtual walk-in clinics from April 1 to December 31, 2020, were compared to Ontario residents who had any virtual family physician visit. The main outcome was postvisit health care use. RESULTS: Virtual walk-in patients (n=132,168) had fewer comorbidities and lower previous health care use than Ontarians with any virtual family physician visit. Virtual walk-in patients were also less likely to have a subsequent in-person visit with the same physician (309/132,168, 0.2% vs 704,759/6,412,304, 11%; standardized mean difference [SMD] 0.48), more likely to have a subsequent virtual visit (40,030/132,168, 30.3% vs 1,403,778/6,412,304, 21.9%; SMD 0.19), and twice as likely to have an emergency department visit within 30 days (11,003/132,168, 8.3% vs 262,509/6,412,304, 4.1%; SMD 0.18), an effect that persisted after adjustment and across urban/rural resident groups. CONCLUSIONS: Compared to Ontarians attending any family physician virtual visit, virtual walk-in patients were less likely to have a subsequent in-person physician visit and were more likely to visit the emergency department. These findings will inform policy makers aiming to ensure the integration of virtual visits with longitudinal primary care.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.040
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.179
GPT teacher head0.503
Teacher spread0.325 · 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

Citations31
Published2023
Admission routes3
Has abstractyes

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