An intersectional perspective on the sociodemographic and clinical factors influencing the status of Not in Education, Employment, or Training (NEET) in patients with first-episode psychosis (FEP)
Bibliographic record
Abstract
Purpose: High rates of Not in Education, Employment or Training (NEET) are seen in people with first episode of psychosis (FEP). Sociodemographic and clinical factors were reported to be associated with NEET status in FEP patients. This study follows Intersectionality to examine the independent and additive effects, and most importantly the intersections of sociodemographic and clinical variables concerning NEET status in FEP patients. It was hypothesized that NEET status in FEP patients would be described by the intersection between at least two predictor variables.Methods: Secondary analyses with chi-square tests, multiple logistic regression and Chi-squared Automatic Interaction Detection (CHAID) analyses were performed on 440 participants with FEP.Results: Chi-square tests indicated that patient socioeconomic status and negative symptom severity were significantly and independently associated with their NEET status. Multiple logistic regression suggested additive effects of age (odds ratio = 1.61), patient socioeconomic status (odds ratio = 1.55) and negative symptom severity (odds ratio = 1.75) in predicting patients’ NEET status. CHAID detected an intersection between patients’ negative symptom severity and socioeconomicstatus in shaping their NEET status.Conclusion: This study explored how the NEET status of patients with FEP was explained not only by the separate effects of negative symptom severity and socioeconomic status but also by the unique intersections of their clinical and social identities. Findings indicated that functional outcomes of patients appear co-constructed by the intersections of multiple identities. Crucial clinical implications of complementing care for negative symptom severity with vocational resources to improve functional outcomes of patients are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".