Global prevalence of antidepressant utilization in the community: A protocol for a systematic review
Bibliographic record
Abstract
ABSTRACT Background Antidepressant drugs are the most frequently prescribed medication for mental disorders. They are also used off-label and for non-psychiatric indications. Prescriptions of antidepressants have increased in the last decades, but no systematic review exists on the extent of their use in the community. Methods and analysis We will conduct a systematic review to estimate the prevalence of antidepressant use in the community. We will search for studies published from 2010 in the Embase and MEDLINE databases. Study selection (by title/abstract and full-text screening) and data extraction for included studies will be independently conducted by pairs of reviewers. We will then synthesize the data on the prevalence of antidepressant use in individuals living in the community. If possible, we will perform a meta-analysis to generate prevalence-pooled estimates. If the data allows it, we will conduct subgroup analyses by antidepressant class, age, sex, country or other sociodemographics. We will evaluate the risk of bias for each included study through a quality assessment using the Joanna Briggs Institute Critical Appraisal tool: Checklist for Studies Reporting Prevalence Data. DistillerSR software will be used for the management of this review. Ethics and dissemination Ethical approval is not required for this review as it will not involve human or animal subjects. The findings of our systematic review will be disseminated through publications in peer-reviewed journals, the Qualaxia Network ( https://qualaxia.org ), presentations to international conferences on mental health and pharmacoepidemiology, as well as general public events. PROSPERO registration details CRD42021247423
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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 | 0.107 | 0.110 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.082 | 0.010 |
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".