Feasibility and acceptability of a culturally adapted CBT-based ‘animated shorts video series’ for depression and anxiety in people with no or low educational literacy: a pilot study from a low-income country
Bibliographic record
Abstract
BACKGROUND: Low educational literacy is associated with high rates of mental health problems. In Pakistan, only 60% of the population is literate. Traditional CBT requires literacy skills. Interventions to address the literacy barriers need to be developed. AIMS: To evaluate the feasibility, acceptability, and preliminary efficacy of a culturally adapted CBT-based animated 'Shorts' series for depression and anxiety in individuals with no or low educational literacy. METHOD: This randomized, rater-blind randomized controlled trial (RCT) compared an animated Shorts series and treatment as usual (TAU) with TAU alone in Pakistan. The primary outcomes were feasibility (recruitment, retention, adherence to treatment and trial processes) and acceptability (drop-outs and participants' feedback). The secondary outcomes included the Hospital Anxiety and Depression Scale (HADS) and the WHO Disability Assessment Schedule 2 (WHODAS 2). Thirty consenting participants were randomly allocated to one of the groups in a 1:1 ratio and were assessed at baseline and the end of the intervention at 12 weeks. RESULTS: The intervention was feasible and acceptable and was successful in reducing the symptoms of depression and anxiety. However, these findings need to be further confirmed in a larger RCT. CONCLUSIONS: These preliminary findings are encouraging, and if future studies confirm that this approach can work, we should be able to overcome the literacy barrier in low- and middle-income countries.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".