Experiences of Psychosis Among Transition-Age Youth Attending an Outpatient Clinic in a Low-Resourced Community
Bibliographic record
Abstract
Objective: This study investigated whether transition-age youth attending a community-based urban outpatient clinic experienced psychosis. The study also explored how those who experienced psychosis, and their social support networks, manage these experiences. Method: Data were collected as part of a feasibility trial of a psychosocial intervention for youth with mental health disorders. The sample included 55 participants ages 16–20. We used Colorado Symptom Index (CSI) psychosis items to corroborate open-ended self-report items on psychosis. Results: Of participants, 29% reported experiences of psychosis. An additional 59% did not endorse psychotic experiences but reported psychotic-like symptoms on the CSI measure. Participants who reported psychotic experiences scored higher on the CSI than those who did not report psychotic experiences. They also reported delayed disclosure, negative emotions, negative messages from family members about their experiences, and coping mechanisms. Conclusions: Multiple assessment strategies are needed to assess for psychotic experiences among youth attending clinics that do not specialize in psychotic illnesses. Social workers in low-resourced clinics are uniquely positioned to identify early stages of psychosis, provide tailored psychosocial interventions, and refer youth to specialized early psychosis clinics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".