Perceptions of and Preferences for Telemedicine Use Since the Early Stages of the COVID-19 Pandemic: Cross-Sectional Survey of Patients and Physicians
Bibliographic record
Abstract
BACKGROUND: While the use of telemedicine (TLM) increased worldwide during the early phases of the COVID-19 pandemic, little is known about the use and acceptance of TLM post the COVID-19 pandemic. OBJECTIVE: This study aims to evaluate patients' and physicians' self-reported use, preferences, and acceptability of different types of TLM after the initial phases of the COVID-19 pandemic. METHODS: We conducted a cross-sectional survey among patients and physicians in Geneva, Switzerland, between September 2021 and January 2022. Patients in waiting rooms of both private and public medical centers and emergency services were invited to answer a web-based questionnaire. Physicians working in private and public settings were invited by email to answer a similar questionnaire. The questionnaires assessed participants' sociodemographics and digital literacy; self-reported use of TLM; as well as preferences and acceptability of TLM for different clinical situations. RESULTS: A total of 567 patients (309/567, 55% women) and 448 physicians (230/448, 51% women and 225/448, 50% in private practice) responded to the questionnaire. Patients (263/567, 46.5%) and physicians (247/448, 55.2%) generally preferred the phone over other TLM formats and considered it to be acceptable for most medical situations. Email (417/567, 73.6% and 308/448, 68.8%) was acceptable for communicating exam results, and medical certificates (327/567, 67.7% and 297/448, 66.2%) and video (302/567, 53.2% and 288/448, 64.3%) was considered acceptable for psychological support by patients and physicians, respectively. Older age was associated with lower acceptability of video for both patients and physicians (odds ratio [OR] 0.03, 95% CI 0.00-0.33 and OR 0.23, 95% CI 0.08-0.66) while previous use of video was positively associated with video acceptability (OR 3.16, 95% CI 1.84-5.43 and OR 3.34, 95% CI 2.91-5.54). Psychiatrists and hospital physicians were more likely to consider video to be acceptable (OR 10.79, 95% CI 3.96-29.30 and OR 3.97, 95% CI 2.23-7.60). CONCLUSIONS: Despite the development of video, the acceptability of video remains lower than that of the phone for most health issues or patient requests. There is a need to better define for which patients and in which medical situations video can become safe and efficient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".