Providers’ Perspectives on Telemental Health Usage After the COVID-19 Pandemic: Retrospective Analysis
Bibliographic record
Abstract
BACKGROUND: Mental health care pivoted to telemedicine during the COVID-19 pandemic, and there is uncertainty around the sustainability of this rapid shift. OBJECTIVE: This study examined how intentions to continue using telemedicine after the COVID-19 pandemic are influenced by provider perceptions of usefulness, ease of use, and professional social influence, facilitating organizational conditions. METHODS: We conducted a web-based, cross-sectional survey of 369 telemental health providers between February and March 2021. A hierarchical linear regression analysis was conducted to predict intentions to continue using telemedicine after the COVID-19 pandemic. RESULTS: Most providers began using telemedicine in March 2020 or later (257/369, 69.6%) and attended to ≥50% of their clients via telemedicine (299/369, 81.0%). Intention to continue using telemedicine after the COVID-19 pandemic was predicted by the telemedicine caseload (β=.10; P=.005), perceived usefulness in general (β=.10; P=.008), ease of use (β=.08; P=.04), social influence (β=.68; P<.001), and facilitating conditions (β=.08; P=.047). CONCLUSIONS: Exploration of the predictors of telemedicine usage beyond the COVID-19 pandemic aids in surveillance of telemedicine usage, integration with future clinic workflows, and the shaping of public policy. It is important to consider telemedicine services as not only a response to a crisis but also an effective and useful solution for everyday life. Our results suggest widespread, sustainable telemedicine adoption.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".