Preliminary evaluation of a culturally adapted CBT-based online programme for depression and anxiety from a lower middle-income country
Bibliographic record
Abstract
Abstract Online cognitive behaviour therapy (CBT), self-help and guided self-help (GSH) interventions have been found to be efficacious and cost-effective for treatment of anxiety and depression, but there are limited data from low- and middle-income countries on culturally adapted digital interventions for these common mental disorders. The aim of this study was to investigate the feasibility and acceptability of an online culturally adapted CBT-based guided self-help (CaCBT-GSH) for patients with anxiety and depression in Pakistan. This randomized controlled trial recruited 39 participants from primary care in Karachi, Pakistan and randomized them to two groups. The intervention group received seven modules of CaCBT-GSH plus treatment as usual (TAU) over 12 weeks. The control group was a waitlist control plus TAU. The primary outcomes were feasibility and acceptability. Clinical outcomes included results from the Hospital Anxiety and Depression Scale (HADS) and the WHO Disability Assessment Schedule 2 (WHODAS 2). Assessments were carried out at baseline and at 12 weeks. All 39 individuals who met eligibility criteria for the study agreed to participate. Adherence to the intervention was excellent, with 85% (17/20) completing more than five modules. Statistically significant improvements were found in all clinical outcomes in the intervention group. This was the first trial of an online CaCBT-GSH intervention, which was found to be feasible and acceptable to Pakistani patients with anxiety and depression. CaCBT-GSH may help improve symptoms, depression, anxiety and overall functioning in this population. The results provide rationale for a larger, confirmatory randomized controlled trial of digital CaCBT-GSH. Key learning aims (1) Leveraging digital and virtual platforms to deliver psychosocial interventions may contribute to addressing the significant treatment gap in low-resource settings. (2) CBT-informed guided self-help is feasible and acceptable in the treatment of common mental disorders in Pakistan. (3) The results of this study merit a larger, appropriately powered confirmatory randomized controlled trial to determine clinical and cost effectiveness.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".