Patient Telemedicine Perceptions During the COVID-19 Pandemic Within a Multi-State Medical Institution: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, to prevent the spread of the virus, federal regulatory barriers around telemedicine were lifted, and health care institutions encouraged patients to use telemedicine, including video appointments. Many patients, however, still chose face-2-face (f2f) appointments for nonemergent clinical care. OBJECTIVE: We explored patients' personal and environmental barriers to the use of video appointments from April 2020 to December 2020. METHODS: We conducted qualitative telephone interviews of Mayo Clinic patients who attended f2f appointments at the Mayo Clinic from April 2020 to December 2020 but did not utilize Mayo Clinic video appointment services during that time frame. RESULTS: We found that, although most patients were concerned about preventing COVID-19 transmission, they trusted Mayo Clinic to keep them safe when attending f2f appointments. Many expressed that a video appointment made it difficult to establish rapport with their providers. Other common barriers to video appointments were perceived therapeutic benefits of f2f appointments, low digital literacy, and concerns about privacy and security. CONCLUSIONS: Our study provides an in-depth investigation into barriers to engaging in video appointments for nonemergent clinical care in the context of the COVID-19 pandemic. Our findings corroborate many barriers prevalent in the prepandemic literature and suggest that rapport barriers need to be analyzed and problem-solved at a granular level.
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 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".