Bibliographic record
Abstract
During the 2020 lockdown in response to COVID-19, students in the Master of Psychotherapy at the Auckland University of Technology (AUT) were required to rapidly move their clinical work online. We surveyed these students about their experience of working clinically online. We used a mixed-methods approach and analysed qualitative data using grounded theory methods. Students found the move online difficult, with technological challenges, the loss of a professional clinical space, and having to establish and maintain the therapeutic alliance in the unfamiliar online setting. They showed a strong preference for in-person clinical work, along with scepticism about the efficacy of online therapy, though some acknowledged its convenience and others its currency and relevance. Most expressed a need for more specific training in online therapy. Students rated their technological skill level higher than their levels of interest in online communication. This suggests that preferences, rather than technical skill, influenced their hesitancy for working clinically online. While online therapy can impose increased strain on clinicians and directly impact their capacity to manage online clinical work, the literature finds strong and consistent evidence that online therapy has equivalent outcomes to in-person therapy. There is significant emphasis in the literature on the disjunct between the outcomes evidence and therapist expectations. This is modified somewhat by training and experience in online therapy. We recommend that research- active psychotherapists engage actively and collaboratively with the profession, through professional bodies, to encourage research-informed professional development and practice for clinicians; and that further research is conducted into effective strategies for training in online clinical delivery.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.663 | 0.484 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".