Preventing Schizophrenia and Psychotic Behaviour: Definitions and Methodological Issues
Bibliographic record
Abstract
Although schizophrenia onset usually occurs in late adolescence or early adulthood, much research shows that its seeds are planted early in life and that eventual onset occurs at the end of a neurodevelopmental process leading to aberrant brain functioning. This idea, along with the fact that current therapies are far from fully effective, suggests that preventive treatments may be needed to achieve an ideal outcome for schizophrenia patients and those predisposed to the disorder. In this article, we review the methodological challenges that must be overcome before effective preventive interventions can be created. Prevention studies will need to define the target population. This requires the identification of risk factors that will be useful in selecting at-risk people for preventive treatment. We review currently identified risk factors for schizophrenia: genes, psychosocial factors, pregnancy and delivery complications, and viruses. We also review 3 different types of prevention programs: universal, indicated, and selective. For schizophrenia, we distinguish prevention programs that target prodromal cases and those that target the disorder's premorbid precursors. Although those targeting prodromal cases provide a useful framework for early treatment of the disorder, studies of premorbid individuals are needed to design a truly preventive treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".