Predictors of persisting psychotic like experiences in children and adolescents: A scoping review
Bibliographic record
Abstract
BACKGROUND: Subclinical psychotic experiences (PLEs) are among the frequently reported mental health problems in children/adolescents. PLEs identified in cross sectional studies of children/adolescents are associated with current and future mental health problems. These associations are stronger for PLEs that persist over time. Hence, it could be useful to examine which children/adolescents with PLEs at a first assessment (baseline) are more likely to have PLEs at subsequent assessments. METHODS: We conducted a scoping review of studies that examined whether characteristics of children/adolescents (≤18 years) with PLEs at baseline predict whether PLEs are likely to be persistent or remittent at subsequent assessments. We included studies published between January 2002 and December 2017, conducted on general child/adolescent populations of ≥300 individuals, that provided data on PLEs for at least 2 time points, had available follow-up data for ≥50% of those assessed for PLEs at baseline and targeted for follow-up examination, and reported the differences between individuals with PLEs that persisted or remitted during the study period. RESULTS: Six studies met our criteria. Each of them investigated a wide range of baseline characteristics but no predictor of persistence was replicated. CONCLUSIONS: Our knowledge about which children/adolescents with PLEs at an initial assessment are likely to have persistent PLEs at subsequent assessments is sparse. A handful of predictors of persistent PLEs have been investigated so far, and none replicated. A better understanding of these predictors would be an important complement to investigations examining the evolution of PLEs and of mental health problems in children/adolescents.
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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 | 0.008 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".