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Record W4213024695 · doi:10.1093/jcag/gwab049.081

A82 DELIVERY OF AMBULATORY CARE DURING THE COVID-19 PANDEMIC IN THE DIVISION OF DIGESTIVE CARE & ENDOSCOPY, HALIFAX, NS

2022· article· en· W4213024695 on OpenAlexaffabout
Jordan Francheville, Prosper Koto, Kevork Peltekian

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsMedicineAmbulatory carePandemicHealth careAmbulatoryOddsObservational studyDescriptive statisticsOdds ratioFamily medicineLogistic regressionEmergency medicineCoronavirus disease 2019 (COVID-19)Medical emergencyDiseaseInternal medicineStatistics

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 pandemic has placed the Canadian healthcare system under substantial strain requiring rapid and systemic changes to healthcare delivery in gastroenterology ambulatory care, including a shift to providing synchronous clinical visits virtually. It is important to describe and evaluate the impact of this care delivery change on patients, providers and the healthcare system in order to improve the quality of virtual care in the future. Aims As part of a larger quality improvement initiative, the aim of this project was to better understand the health system impact of the shift from in-person to virtual care delivery in the Division of Digestive Care & Endoscopy in Halifax, NS. Methods Using a before-and-after observational study design, outpatient encounters from January-March 2020 (Pre-COVID) were compared to encounters after the pandemic restrictions began April-June 2020 (COVID-Impacted). The primary objective was to compare the proportion of synchronous clinic encounters in the gastroenterology ambulatory space conducted virtually before versus after pandemic restrictions were implemented. Secondary objectives were to determine whether patient, disease, or provider-specific factors were associated with virtual care visits or changed with the implementation of pandemic restrictions. Endoscopic encounters were excluded. Descriptive statistics were used to compare patient and encounter characteristics in the Pre-COVID and COVID-Impacted periods. Multiple logistic regression modeling was used to evaluate the association between patient and provider characteristics and use of virtual care delivery. Unadjusted and adjusted odds ratio with associated 95% CI were estimated. Results A total of 4,923 unique patients (60.1% Pre-COVID and 39.9% in the COVID-Impacted period) and 6,659 encounters were identified. The proportion of synchronous clinical visits conducted virtually increased after February 2020, increasing from 25% (Pre-COVID) to 91% (COVID-Impacted). The Pre-COVID versus COVID-Impacted periods also differed with respect to median patient age (56 vs. 59, P = 0.000), mean proximity to the hospital (40km vs. 48km, P = 0.007) and proportion of new consults deemed urgent (9.8% vs. 20.0%, P = 0.000). Patients with family physicians, return visits, and patient age greater than 65 years were associated with the use of synchronous virtual care visits. Conclusions This project details the abrupt and significant disruption in in-person ambulatory, non-endoscopic digestive care and the dramatic uptake in virtual care delivery as a result of COVID-19 restrictions in Halifax, NS. Future research will explore virtual care use as pandemic restrictions ease to inform how virtual care is integrated into post-pandemic practice to guide new standards of care. Funding Agencies None

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)0.001
metaresearch head score (Gemma)0.002
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 score0.167
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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