Therapeutic alliance in videoconferencing psychotherapy: A review
Bibliographic record
Abstract
Psychotherapy services are limited in remote and rural areas in Australia and across the globe. Videoconferencing has become well established as a feasible and acceptable mode of psychological treatment delivery. Therapeutic alliance (TA) is an essential factor underlying successful therapy across therapeutic models. In order to determine the state of knowledge regarding TA in psychotherapy via videoconferencing, a literature review was conducted on research studies that formally measured TA as primary, secondary or tertiary outcome measures over the past 23 years. The databases searched were Medline, PsycArticles, PsycINFO, PsycEXTRA and EMBASE. Searching identified 9915 articles that measured satisfaction, acceptability or therapeutic rapport, of which 23 met criteria for the review. Three studies were carried out in Australia, 11 in USA, 4 in Canada, 3 in Scotland and 2 in England. Studies overwhelmingly supported the notion that TA can be developed in psychotherapy by videoconference, with clients rating bond and presence at least equally as strongly as in-person settings across a range of diagnostic groups. Therapists also rated high levels of TA, but often not quite as high as that of their clients early in treatment. The evidence was examined in the context of important aspects of TA, including bond, presence, therapist attitudes and abilities, and client attitudes and beliefs. Barriers and facilitators of alliance were identified. Future studies should include observational measures of bond and presence to supplement self-report.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".