Treatment Without Cost? Effects and Side Effects of an Internet-Based Intervention for Depression: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Internet-based interventions for depression are increasingly integrated into health care due to their effectiveness, availability, and cost-effectiveness. However, negative effects have largely been ignored. Objective: This study aimed to evaluate both positive and negative effects of an unguided intervention. Methods: In total, 303 participants were analyzed using mixed models for repeated measures to assess changes in depressive symptoms via Beck Depression Inventory-II (primary outcome) after 12 weeks compared to waitlist controls with care as usual. Secondary endpoints included depressive symptoms (Patient Health Questionnaire-9 [PHQ-9]), self-esteem, and quality of life. Negative effects were evaluated using the positive and negative effects of psychotherapy scale for internet-based interventions (PANEPS-I). Moderation analyses were conducted to explore influential effects on treatment outcomes. Results: The intervention group showed greater reduction in depressive symptoms compared to controls, with small to medium effect sizes (g=0.30-0.42) with averaged 14 logins. Although improvements in self-esteem and quality of life were not observed in intention-to-treat analyses, the completer sample indicated higher self-esteem in the intervention group. Negative effects were reported by 22% (22/100) to 68% (66/97), with the highest rates for program-related effects (eg, not addressing personal problems). No moderation effects were identified. Conclusions: The intervention effectively reduces depressive symptoms. Although negative effects were present, they did not impact treatment outcome, tentatively suggesting that overall benefits may outweigh the negative effects for users.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".