Dialectical Behavioural Therapy (DBT)? There is an App for That!!
Bibliographic record
Abstract
Introduction Borderline personality disorder (BPD) is a common psychiatric disorder. Although DBT has been proven effective, in the treatment of this disorder, many patients suffering from BPD are resistant to taking part in group psychotherapy, a core aspect of DBT. With Internet use ever rising, online DBT could be an alternative effective treatment for these group of patients. Objectives Understand the barriers in receiving DBT in patients with personality disorder and understand how online DBT could be helpful in improving these patients' symptoms Aim Use online DBT as an alternative method for patients who do not have access to live DBT Method We gave the participants the opportunity to choose the online format or the live sessions for Managing Powerful Emotions program at Personality Disorders Service at Queen's university. On each session, patients were provided with information on a topic, homework sheets and feedback from the therapist. All the participants were assessed by using 'self assessment questionnaire' and Difficulties in Emotion Regulation Scale (DERS). Results The DERS scores among these two groups were not significantly different before the treatment. Statistical analysis showed that both online and live DBT significantly reduced DERS scores in all 6 categories and there was no significant differences in amount of the changes in the scores in the 2 groups. Conclusion Despite the proven efficacy of psychotherapy, there are some barriers, including being resistant to taking part in live sessions, long wait-lists and transportation. With Internet use rising, delivering online psychotherapy might be an alternative method.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".