Lessons Learned: Virtual DBT from Therapist and Client Perspectives
Bibliographic record
Abstract
With the abrupt ramp-down of in-person mental health outpatient visits due to COVID-19, rapid adaptation was required to transform Dialectical Behaviour Therapy (DBT) services for adolescents and their families to a virtual format Within two weeks the DBT team changed both group and individual DBT therapy to an on-line platform There is little literature regarding delivery of DBT in this way, Experience of patients, families and providers to this change was subsequently evaluated Methods: Delivery of group and individual DBT components was done via PHIPA compliant Zoom platform A secure anonymous feedback survey was administered via RedCap to patients, families, and therapy providers Focus group interviews were conducted to obtain qualitative feedback Ongoing learning about technical challenges was shared regularly through the team and supported by the hospital's Virtual Care Working Group Results: After a steep learning curve, clients and families expressed satisfaction with the experience of Virtual Multifamily DBT, both in full DBT and Lite (skills groups only) formats Time savings, cost savings, and ease of use were identified as positives The learning experience was rated as positive Providers and clients both expressed preference for in-person groups, but remained positive for the most part about the virtual experience Both providers and clients identified technical problems as the biggest challenge Conclusions: Dialectical Behaviour Therapy may successfully be offered to young people and their caregivers by videoconferencing platforms Access to DBT can be increased by the removal of obstacles inherent to in-person group treatment
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".