Health Care Providers’ Perceptions of Quality, Acceptance, and Satisfaction With Telebehavioral Health Services During the COVID-19 Pandemic: Survey-Based Study
Bibliographic record
Abstract
BACKGROUND: Due to rapidly increasing rates of COVID-19 across the country, system-wide changes were needed to protect the health and safety of health care providers and consumers alike. Technology-based care has received buy-in from all participants, and the need for technological assistance has been prioritized. OBJECTIVE: The objective of this study was to determine the initial perceptions and experiences of interprofessional behavioral health providers about shifting from traditional face-to-face care to virtual technologies (telephonic and televideo) during the COVID-19 pandemic. METHODS: A survey-based study was performed at a large, integrated medical health care system in West-Central Florida that rapidly implemented primary care provision via telephone and televideo as of March 18, 2020. A 23-item anonymous survey based on a 7-point Likert scale was developed to determine health care providers' perceptions about telephonic and televideo care. The survey took 10 minutes to complete and was administered to 280 professionals between April 27 and May 11, 2020. RESULTS: In all, 170 respondents completed the survey in entirety, among which 78.8% (134/170) of the respondents were female and primarily aged 36-55 years (89/170, 52.4%). A majority of the respondents were outpatient-based providers (159/170, 93.5%), including psychiatrists, therapists, counselors, and advanced practice nurses. Most of them (144/170, 84.7%) had used televideo for less than 1 year; they felt comfortable and satisfied with either telephonic or televideo mode and that they were able to meet the patients' needs. CONCLUSIONS: Our survey findings suggest that health care providers valued televideo visits equally or preferred them more than telephonic visits in the domains of quality of care, technology performance, satisfaction of technology, and user acceptance.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".