Personality traits in early psychosis: relationship with symptom and coping treatment outcomes
Bibliographic record
Abstract
AIMS: This study aimed to determine personality profiles of individuals with early psychosis based on the Five Factor Model of personality and assess the predictive value of personality traits or profiles on therapeutic outcomes of two group treatments for recent onset psychosis: cognitive behaviour therapy or skills training for symptom management. METHODS: One hundred and twenty-nine individuals with early psychosis were recruited to participate in a randomized controlled trial. The participants were randomized to one of two group treatments or to a wait-list control group. Measures included a personality inventory (NEO Five Factor Inventory) and outcome measures of symptomatology (Brief Psychiatric Rating Scale-Expanded) and coping strategies (Cybernetic Coping Scale). RESULTS: Cluster analyses revealed three different personality profiles (based on the Five Factor Model) - none specifically linked to psychotic symptoms. No links were revealed between personality traits and symptom change scores. Personality traits were linked to therapeutic improvements in active coping strategies, with extraversion accounting for 17% of the variance. Neuroticism was linked to increased use of passive coping strategies. Active coping strategies were also predicted by profile 1 (holding the highest openness score) with 26% of the variance explained and by profile 3 (the highest extraversion score), with 14% of the variance explained. CONCLUSIONS: Individuals with early psychosis can present with distinct personality profiles as would be expected in a non-clinical population. Personality traits do not appear to influence symptomatic treatment outcomes but are linked to behavioural changes, such as the use of coping 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".