A Meta-Research of Randomized Controlled Trials in the Field of Mental Health: Comparing Pharmacological to Non-Pharmacological Interventions from 1955 to 2020
Bibliographic record
Abstract
Objective This study aimed to provide a general overview of mental health randomized controlled trials (RCTs) and summarize the temporal trends in terms of the number of studies, median sample sizes, and median effect sizes using data collected from the Cochrane Database of Systematic Reviews (CDSR). Methods Using data collected from the CDSR, the temporal trends are compared in terms of the number of studies, median sample sizes, and median effect sizes between two broad categories of interventions: pharmacological RCT (ph-RCT) and non-pharmacological RCT (nph-RCT), and in conjunction with major mental disorder categories. Results Chronologically, the number of mental health RCTs reported in publications has increased exponentially from 1955 to 2020. While ph-RCT comprised a majority of mental health RCTs in the earlier years, the proportion of nph-RCTs increased more quickly over time and markedly exceeded ph-RCT after 2010. The median sample size for all 6,652 mental health RCTs was 61, with 61 for ph-RCT and 60 for nph-RCT. Over time, the median fluctuated but an increasing trend was observed over the past 60+ years. The median of the effect size, measured by Pearson's r, for overall RCTs was 0.18, and nph-RCT (0.19) had a larger median effect size compared to ph-RCT (0.16). Over the years, the nph-RCT had a larger median effect size than the ph-RCT. Differences in the median effect sizes among the categories of mental disorders were also noted. Schizophrenia had the most RCTs, with a median Pearson's r value of 0.17. Mood disorder had the second largest number of RCTs and a median Pearson's r value of 0.15. Neurotic/stress-related mental disorder had the third largest number of RCTs with the highest median Pearson's r being 0.23. Conclusions This study provides meaningful information and filled the knowledge gap in mental health RCTs.
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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 | 0.213 | 0.466 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".