Treatment for Depression Among Adults: An Evidence and Gap Map of Systematic Reviews
Bibliographic record
Abstract
OBJECTIVE: To identify and map systematic reviews on the effectiveness of treatment for depressive disorders among adults. METHODS: We retrieved systematic reviews and meta-analyses of randomized controlled trials involving adults with depressive symptoms from twelve English and four Chinese databases (June 21, 2022). Using an interactive map, we visualized the effectiveness of evidence on depression based on an intervention-outcome framework. The interventions included psychotherapy, pharmacotherapy, complementary and alternative treatments, and others. The outcomes included the remission of depressive symptoms, symptoms of depressive disorder, life and social skills, and adverse events. RESULTS: We included 994 systematic reviews and meta-analyses, including 32 that were review protocols, highlighting the distribution of psychotherapy, pharmacotherapy, and complementary and alternative treatments. However, the evidence and gap map (EGM) revealed significant gaps in evidence for specific interventions, populations, outcomes, and regions. While psychotherapy, pharmacotherapy, and complementary and alternative treatments dominate the landscape, the review highlighted a lack of research on interventions for specific types of depression, such as depression in people with bipolar disorder and treatment-resistant depression. It was a similar situation for underserved populations, including young and middle-aged adults, males, sexual minority individuals, and people with disabilities. The map also suggested the need for more research on the potential risks and side effects associated with both pharmacological and nonpharmacological treatments. CONCLUSIONS: The contribution of this EGM was to present the available evidence on psychotherapy, pharmacotherapy, and complementary and alternative treatments for depression in adults, making available an evidence base that could inform future policy decisions and practice. It also identified evidence gaps in interventions, outcomes, population, regions, and evidence confidence. The need for further research on tailored treatments for specific populations was highlighted.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.043 | 0.200 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.067 | 0.043 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".