Experiences of Connecticut Community Health Center Patients With Telemedicine During the COVID-19 Pandemic
Bibliographic record
Abstract
Background Since COVID-19 rapidly made telemedicine a necessity, it is clear that virtual patient visits are going to be part of the new norm in continued outpatient care as we move into a postpandemic reality. However, we are still learning about patients’ experiences and preferences amid the rapid and widespread deployment of telemedicine. Objective The aim of this paper is to determine patients’ satisfaction with and experiences of telehealth services during the COVID-19 pandemic among those seeking care at Community Health Center Association of Connecticut member clinics. Methods Data were collected using a 24-question phone survey, which asked about telehealth use, frequency of telehealth visits, and barriers experienced. Participants were eligible if they aged 18 years or older, were English speaking, and were receiving care at 1 of 3 participating Community Health Center Association of Connecticut clinics. Results A total of 383 participants completed the phone survey throughout July 2021. The median age grouping was between 55 and 59 years, and the majority (63%) were female. Since COVID-19, in total, 78% reported having one or more audio-only telehealth visits (from 31% before), and 53% had one or more video telehealth visit (from 13% before). Most reported being very satisfied with their visits (86%) and that they felt confident in their provider’s ability to address their needs (74%). Most did not experience technical problems or have difficulty understanding how to connect to their provider. Even among older participants (60 years and older), only 28% reported having difficulty understanding how to connect to their provider, compared with 23% of 40- to 59-year-old patients and 18% of 18- to 39-year-old patients. Moreover, 45% reported being very likely to continue using telehealth even after the pandemic. However, 52% would have liked the option of in-person visits if they had been available in the past year. Conclusions These results suggest that most patients find telehealth visits an appropriate and accessible means of accessing health care, though some still like the option of seeing their provider in person. Future work should compare provider and patient experiences and identify optimal means of making the encounters mutually satisfying and beneficial. Conflicts of Interest None declared.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".