Therapist-Supported Online Interventions for Children and Young People With Tic Disorders: Lessons Learned From a Randomized Controlled Trial and Considerations for Future Practice
Bibliographic record
Abstract
In recent years, research into internet-based cognitive behavioral therapy (iCBT) has suggested that therapist-guided digital interventions have greater engagement, adherence, and effectiveness than self-directed digital therapies. While research has focused on the effectiveness of, and adherence to, these interventions, less attention has been paid to their implementation in practice and what aspects of the therapist role support success. An understanding of the key factors related to the therapist role and intervention delivery is required if these iCBTs are to be applied in routine clinical care and outcomes optimized. In light of the coronavirus disease 2019 (COVID-19) pandemic, there is greater emphasis on allowing patients access to remote therapies. We report the experiences and reflections of 4 therapists and their 2 supervisors in delivering an online, therapist-supported intervention in a randomized controlled trial for children and young people with tic disorders (the Online Remote Behavioural Intervention for Tics [ORBIT] trial). Themes discussed include the importance of training, supervision, creating support documents/manuals, and record keeping. Alongside this are communication strategies used by therapists to encourage patient adherence and treatment effectiveness. These include rapport building, treatment personalization, and suggestions for overcoming non-engagement. These reflections offer important considerations for the delivery of iCBTs as well as implications associated with the implementation of these interventions in existing services and future research studies. We share thoughts on where iCBTs may sit in a stepped care model, how services may deal with comorbid conditions, and the potential role of iCBTs in collecting clinical data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.345 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".