Reflections of young people who have had a first episode of psychosis: what attracted them to use alcohol and illicit drugs?
Bibliographic record
Abstract
AIM: To identify factors that contribute to the initiation of alcohol and street drug use from the perspective of people who were enrolled in early intervention programmes for a first episode of psychosis. METHOD: Eight focus groups were conducted involving an average of four to six participants per group, with each group consisting of young people who met provincial inclusion criteria for early intervention programmes. Thematic analysis was used to systematically code transcripts from the focus groups for concepts, patterns and themes related to early use of illicit substances. RESULTS: Participants included 45 young people diagnosed with affective psychosis or non-affective spectrum disorders. Seventy-three percent were male, with a median age of 23 years. In general, substance use was an important topic that emerged across all focus groups. Participants talked about three main factors attracting them to initiate use of substances, most predominantly cannabis: (i) using within a social context; (ii) using as a self-medication strategy; and (iii) using to alter their perceptions. CONCLUSIONS: The need for social relationships, coping strategies and pleasurable experiences appear to be important reasons for initiating substance use. Additional research is needed to identify whether prodromal youth report the same factors that attract them to initiate use in order to develop more effective prevention strategies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".