Psychotherapist experiences with telepsychotherapy: Pre COVID-19 lessons for a post COVID-19 world.
Bibliographic record
Abstract
Psychotherapists accelerated their adoption of telepsychotherapy during the COVID-19 outbreak to accommodate preventative isolation and social distancing. Lessons from psychotherapist experiences with technology prior to the outbreak can offer recommendations for practitioners and professional regulators. In this study, psychotherapists were interviewed about their use of technology in practice and interviews were analyzed for consistency with current literature on usual practice and professional regulations. The researchers used actor-network theory to map and explore the links and themes that emerged from the research. We found that technology use was more integrated with psychotherapy practice and psychotherapists were more confident and comfortable with telepsychotherapy than the literature predicted. Key themes arising from the interviews were psychotherapist responsibility and trust that included expanded psychotherapist responsibility, client trust, psychotherapists' self-trust, and trust of information sources. Telepsychotherapy can be enhanced by reflective, intentional practice, making space to examine routine behaviors, and developing strategies to counteract the unreliability of technology. Further, professional and regulatory bodies can support effective practice by developing clear and achievable technological competence responsibilities and by integrating technology training with mandatory psychotherapy education.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".