A comparative analysis of age of onset and associated factors in non-refugee immigrants versus native-born individuals with psychotic disorders
Bibliographic record
Abstract
Background: The age of onset in psychosis (AOP) has been of particular interest in research due to its association with the prognosis and expression of psychotic disorders. There is a lack of research concerning the influence of environmental factors such as stressful life events or exposure to traumatic events on AOP, especially among non-refugee immigrants.Objective: This study investigates the AOP among non-refugee immigrants as compared to native-born individuals and explores the associations of AOP with trauma burden. A total of 198 participants (99 non-refugee immigrants and 99 native-born individuals) diagnosed with psychotic disorders were assessed for sociodemographic, clinical, and migration data, along with trauma exposure, using validated scales.Methods: A multiple linear regression model was used to assess potential associations between AOP and these variables.Results: Non-refugee immigrants experience psychosis at an earlier age (25.26 vs. 28.22 years) and exhibit unique associations between AOP and factors such as age at first migration, cumulative trauma distress, stressful events, and comorbid psychiatric diagnosis. Conversely, native-born individuals show associations with sex, age, job status, and comorbid psychiatric diagnoses.Conclusions: These findings emphasise the significance of considering migration-related factors and trauma in understanding AOP among non-refugee immigrants, providing valuable insights for tailored preventive interventions in this population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".