Assessing patients' attitudes towards telepsychotherapy: The development of the unified theory of acceptance and use of technology‐patient version
Bibliographic record
Abstract
This study presents the development of a self-report measure of patients' attitudes towards telepsychotherapy. The measure is based on a well-researched model of attitudes towards using technology, the Unified Theory of Acceptance and Use of Technology (UTAUT) framework (Venkatesh et al., 2003). We examined the psychometric properties of the UTAUT adapted for psychotherapy patients (UTAUT-P) in a sample of 107 psychotherapy patients who received telepsychotherapy via video conferencing during the COVID-19 pandemic. Exploratory factor analysis resulted in a 14-item UTAUT-P version, with four factors-(1) Therapy Quality Expectancy, (2) Convenience, (3) Ease of Use, and (4) Pressure from Others-and was further corroborated by the results of the confirmatory factor analysis. Our results indicated the four-factor model's adequate fit to the data and demonstrated adequate construct validity and reliability of the UTAUT-P factors. All factors, except for Ease of Use, were significantly and positively associated with intention to use telepsychotherapy technology in the future. This study complements the research on therapists' attitudes towards telepsychotherapy, based on the therapist version of the UTAUT. The developed 14-item UTAUT-P might be a helpful, brief self-report tool in clinical practice, which might give the patient a voice around the potential use of telepsychotherapy technology in their care. This initial application of the UTAUT-P patients during the COVID-19 pandemic offers a building block for future research on patients' attitudes towards telepsychotherapy, outside the context of a forced transition.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".