Patient-Oriented Research to Improve Internet-Delivered Cognitive Behavioural Therapy for People of Diverse Ethnocultural Groups in Routine Practice
Bibliographic record
Abstract
There has been limited research on improving Internet-delivered Cognitive Behavioural Therapy (ICBT) in routine online therapy clinics that serve people from diverse ethnocultural groups (PDEGs). This article describes a patient-oriented adaptation approach used to address this gap in research. A working group consisting of people with lived experience, community representatives, ICBT clinicians, managers, and researchers was formed. The working group examined archival feedback on ICBT from past patients who self-identified as being from diverse ethnocultural backgrounds (N = 278) and the results of interviews with current patients (N = 16), community representatives (N = 6), and clinicians (N = 3). The archival data and interviews revealed the majority of the patients reported being satisfied with and benefitting from ICBT. Suggestions for improvement were not related to the cognitive-behavioural model and techniques, but rather to making treatment materials more inclusive. Consequently, the ICBT adaptation focused on adding content related to cultural influences on mental health, addressing stigma, diversifying case stories, examples, and imagery, adding audiovisual introductions, and replacing English idioms with more descriptive language. Moreover, further training was offered to clinicians, and efforts were made to improve community outreach. This study demonstrates a process for using patient-oriented research to improve ICBT within routine care serving patients of diverse backgrounds.
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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 | 0.040 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".