Quality and Methodological Challenges in Internet-Based Mental Health Trials
Bibliographic record
Abstract
OBJECTIVE: To review the quality of Internet-based mental health intervention studies and their methodological challenges. MATERIALS AND METHODS: We searched multiple literature databases to identify relevant studies according to the Population, Interventions, Comparators, Outcomes, and Study Design framework. Two reviewers independently assessed selection bias, allocation bias, confounding bias, blinding, data collection methods, and withdrawals/dropouts, using the Quality Assessment Tool for Quantitative Studies. We rated each component as strong, moderate, or weak and assigned a global rating (strong, moderate, or weak) to each study. We discussed methodological issues related to the study quality. RESULTS: Of 122 studies included, 31 (25%), 44 (36%), and 47 (39%) were rated strong, moderate, and weak, respectively. Only five studies were rated strong for all of the six quality components (three of them were published by the same group). Lack of blinding, selection bias, and low adherence were the top three challenges in Internet-based mental health intervention studies. CONCLUSIONS: The overall quality of Internet-based mental health intervention needs to improve. In particular, studies need to improve sample selection, intervention allocation, and blinding.
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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 | 0.584 | 0.798 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".