Telehealth Use During the COVID-19 Pandemic Among Veterans and Nonveterans: Web-Based Survey Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".