Guided Internet-Delivered Treatment for Depression: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Studies on guided internet-delivered treatment have demonstrated promising results for patients with depressive disorder. OBJECTIVE: The aim of this study was to provide an overview of this research area and identify potential gaps in the research. METHODS: In this scoping review, web-based databases were used to identify research papers published between 2010 and 2022 where guided internet-delivered treatment was administered to participants with depressive disorders, a standardized rating scale of depressive symptoms was used as the primary outcome measure, and the treatment was compared with a control condition. RESULTS: A total of 111 studies were included, and an overview of the studies was provided. Several gaps in the research were identified regarding the design of the studies, treatments delivered, participant representation, and treatment completion. CONCLUSIONS: This review provides a comprehensive overview of the research area, and several research gaps were identified. The use of other designs and active control conditions is recommended. Future studies should provide access to treatment manuals, and more replications should be conducted. Researchers should aim to include underrepresented populations and provide reports of comorbidities. Definitions of adequate dosage, reports of completion rates, and reasons for treatment dropout are recommended for future studies.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".