A Culturally Adapted Cognitive Behavioral Internet-Delivered Intervention for Depressive Symptoms: Randomized Controlled Trial
Bibliographic record
Abstract
Background Internet-delivered treatments for depressive symptoms have proved to be successful in high-income Western countries. There may be potential for implementing such treatments in low- and middle-income countries such as Colombia, where access to mental health services is limited. Objective The objective of this study was to assess the efficacy of a culturally adapted cognitive behavioral internet-delivered treatment for college students with depressive symptoms in Colombia. Methods This was a randomized controlled trial with a 3-month follow-up. The program comprised seven modules. A total of 214 Colombian college students were recruited. They were assessed and randomly assigned to either the treatment group (n=107) or a waiting list (WL) control group (n=107). Participants received weekly support from a trained supporter. The primary outcome was symptoms of depression, as measured by the Patient Health Questionnaire - 9, and the secondary outcomes were anxiety symptoms assessed by the Generalized Anxiety Disorder questionnaire - 7. Other measures, including satisfaction with treatment, were evaluated after 7 weeks. Results Research attrition and treatment dropouts were high in this study. On average, 7.6 sessions were completed per user. The mean time spent on the program was 3 hours and 18 min. The linear mixed model (LMM) showed significant effects after treatment (t197.54=−5.189; P<.001) for the treatment group, and these effects were maintained at the 3-month follow-up (t39.62=4.668; P<.001). Within-group results for the treatment group yielded a large effect size post treatment (d=1.44; P<.001), and this was maintained at the 3-month follow-up (d=1.81; P<.001). In addition, the LMM showed significant differences between the groups (t197.54=−5.189; P<.001). The results showed a large effect size between the groups (d=0.91; P<.001). In the treatment group, 76.0% (16/107) achieved a reliable change, compared with 32.0% (17/107) in the WL control group. The difference between groups was statistically significant (X22=10.5; P=.001). Conclusions This study was the first contribution to investigating the potential impact of a culturally adapted internet-delivered treatment on depressive symptoms for college students as compared with a WL control group in South America. Future research should focus on identifying variables associated both with premature dropout and treatment withdrawal at follow-up. Trial Registration ClinicalTrials.gov NCT03062215; https://clinicaltrials.gov/ct2/show/NCT03062215
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".