User experiences of an online therapist-guided psychotherapy platform, OPTT: A cross-sectional study
Bibliographic record
Abstract
Introduction: In the last few years, online psychotherapy programs have burgeoned since they are a more accessible and scalable treatment option compared to in-person therapies. While these online programs are promising, understanding the user experience and perceptions of care is essential for program optimization. Methods: This study investigated the experiences of end-users who had previously received online psychotherapy through a web-based platform. A 35-item multiple-choice survey was developed by the research team and distributed to past users to capture their perceptions of the program. Results: The survey yielded 163 responses, with a 90 % completion rate. Participants were predominantly white and female, with an average age of 42 years. While most participants preferred in-person therapy, they also reported the benefits of the online psychotherapy program. Participants had positive perceptions of the platform, the quality and interaction of their therapist, and the homework assignments and skills covered. Lack of motivation to complete weekly homework assignments was cited as a common struggle. Discussion: The findings support online psychotherapy as a beneficial digital mental health tool and highlight some areas for improvement. Scalability and accessibility are key benefits of the platform. At the same time, improvements in participant engagement, including those from equity-seeking and equity-deserving groups, may enhance the efficacy of the programs offered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".