How “global” is research in early intervention for psychosis? A bibliometric analysis
Bibliographic record
Abstract
INTRODUCTION: Unlike high-income countries (HICs), there are few early intervention services for psychosis in low-and middle-income countries (LAMICs). In HICs, research spurred the growth of such services. Little is known about the state of EIP research in LAMICs, which we address by examining their research output and collaborations vis-à-vis that of HICs. METHODS: We conducted a search in Scopus database for early psychosis publications in scientific journals since 1980. Data from each record, including title, author affiliation, and date, were downloaded. For HIC-LAMIC collaborations, data on first, corresponding and last authors' affiliations, and funding were manually extracted. Descriptive statistics and social network analysis were conducted. RESULTS: Globally, early psychosis publications increased from 24 in 1980 to 1297 in 2022. Of 16,942 included publications, 16.1 % had LAMIC authors. 71.3 % involved authors from a single country (regardless of income level). 21.9 % were collaborations between HICs, 6.6 % between HICs and LAMICs, and 0.2 % among LAMICs. For research conducted in LAMICs and involving HIC-LAMIC collaborations, the first, last, and corresponding authors were LAMIC-based in 71.8 %, 60.7 %, and 63.0 %, respectively. These positions were dominated (80 %) by authors from four LAMICs. 29.4 % of the HIC-LAMIC subset was funded solely by LAMIC funders, predominantly two LAMICs. CONCLUSIONS: LAMICs are starkly underrepresented in the otherwise flourishing body of early psychosis research. They have far fewer collaborations and less funding than HICs. Closing these gaps in LAMICs where most of the world's youth live is imperative to generate the local knowledge needed to strengthen early psychosis services that are known to improve outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.068 | 0.115 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".