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Record W4385471159 · doi:10.2196/42217

Telehealth Use During the COVID-19 Pandemic Among Veterans and Nonveterans: Web-Based Survey Study

2023· article· en· W4385471159 on OpenAlexvenueno aff
Holly Shoemaker, Alistair Thorpe, Vanessa Stevens, Jorie Butler, Frank A. Drews, Nicole Burpo, Laura D. Scherer, Angela Fagerlin

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersAmerican Heart AssociationU.S. Department of Veterans Affairs
KeywordsTelehealthPandemicMedicineOddsOutreachHealth carePreferenceFamily medicineCoronavirus disease 2019 (COVID-19)TelemedicineLogistic regression

Abstract

fetched live from OpenAlex

BACKGROUND: In the first year of the COVID-19 pandemic, studies reported delays in health care usage due to safety concerns. Delays in care may result in increased morbidity and mortality from otherwise treatable conditions. Telehealth provides a safe alternative for patients to receive care when other circumstances make in-person care unavailable or unsafe, but information on patient experiences is limited. Understanding which people are more or less likely to use telehealth and their experiences can help tailor outreach efforts to maximize the impact of telehealth. OBJECTIVE: This study aims to examine the characteristics of telehealth users and nonusers and their reported experiences among veteran and nonveteran respondents. METHODS: A nationwide web-based survey of current behaviors and health care experiences was conducted in December 2020-March 2021. The survey consisted of 3 waves, and the first wave is assessed here. Respondents included US adults participating in Qualtrics web-based panels. Primary outcomes were self-reported telehealth use and number of telehealth visits. The analysis used a 2-part regression model examining the association between telehealth use and the number of visits with respondent characteristics. RESULTS: There were 2085 participants in the first wave, and 898 (43.1%) reported using telehealth since the pandemic began. Most veterans who used telehealth reported much or somewhat preferring an in-person visit (336/474, 70.9%), while slightly less than half of nonveterans (189/424, 44.6%) reported this preference. While there was no significant difference between veteran and nonveteran likelihood of using telehealth (odds ratio [OR] 1.33, 95% CI 0.97-1.82), veterans were likely to have more visits when they did use it (incidence rate ratio [IRR] 1.49, 95% CI 1.07-2.07). Individuals were less likely to use telehealth and reported fewer visits if they were 55 years and older (OR 0.39, 95% CI 0.25-0.62 for ages 55-64 years; IRR 0.43, 95% CI 0.28-0.66) or lived in a small city (OR 0.63, 95% CI 0.43-0.92; IRR 0.71, 95% CI 0.51-0.99). Receiving health care partly or primarily at the Veterans Health Administration (VA) was associated with telehealth use (primarily VA: OR 3.25, 95% CI 2.20-4.81; equal mix: OR 2.18, 95% CI 1.40-3.39) and more telehealth visits (primarily VA: IRR 1.5, 95% CI 1.10-2.04; equal mix: IRR 1.57, 95% CI 1.11-2.24). CONCLUSIONS: Telehealth will likely continue to be an important source of health care for patients, especially following situations like the COVID-19 pandemic. Some groups who may benefit from telehealth are still underserved. Telehealth services and outreach should be improved to provide accessible care for all.

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)0.003
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.258
GPT teacher head0.515
Teacher spread0.258 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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