Clinicians’ Attitudes Toward Telepsychology in Addiction and Mental Health Services, and Prediction of Postpandemic Telepsychology Uptake: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has resulted in unprecedented uptake of telepsychology services; however, clinicians have mixed attitudes toward virtual technologies. OBJECTIVE: This study (1) explored clinicians' experiences of and intentions to use video, telephone, and in-person services, and (2) tested the utility of the unified theory of acceptance and use of technology (UTAUT) to predict clinicians' intentions to offer telepsychology after the COVID-19 pandemic. METHODS: Clinician satisfaction and therapeutic alliance were compared across in-person, video, and telephone services, while technology attitudes and intention to use after the pandemic were compared across video and telephone services among 118 addiction and mental health clinicians during the COVID-19 pandemic. RESULTS: Clinicians reported more positive experiences with in-person services than both virtual technologies; further, clinicians reported greater positive experiences, attitudes, and intentions to use video services than telephone services across measures. Based on the UTAUT, performance expectancy positively predicted concurrent intentions to use video services (β=0.46; P<.001) and telephone services (β=0.35; P<.001) after the pandemic. Social influence (β=0.24; P=.004) and facilitating conditions (β=0.19; P=.03) additionally predicted the intention to use telephone services. CONCLUSIONS: Clinicians rated in-person services more positively than virtual technologies, with video services perceived more positively than telephone services. Performance expectancy was the primary facilitator of the uptake of both virtual modalities.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| 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".