Life engagement in people living with schizophrenia: predictors and correlates of patient life engagement in a large sample of people living in the community
Bibliographic record
Abstract
BACKGROUND: Life engagement represents a holistic concept that encompasses outcomes reflecting life-fulfilment, well-being and participation in valued and meaningful activities, which is recently gaining attention and scientific interest. Despite its conceptual importance and its relevance, life engagement represents a largely unexplored domain in schizophrenia. The aims of the present study were to independently assess correlates and predictors of patient life engagement in a large and well-characterized sample of schizophrenia patients. METHODS: To assess the impact of different demographic, clinical, cognitive and functional parameters on life engagement in a large sample of patients with schizophrenia, data from the social cognition psychometric evaluation project were analyzed. RESULTS: Overall schizophrenia and depressive symptom severity, premorbid IQ, neurocognitive performance, social cognition performance both in the emotion processing and theory of mind domains, functional capacity, social skills performance and real-world functioning in different areas all emerged as correlates of patient life engagement. Greater symptom severity and greater impairment in real-world interpersonal relationships, social skills, functional capacity and work outcomes emerged as individual predictors of greater limitations in life engagement. CONCLUSIONS: Life engagement in people living with schizophrenia represents a holistic and complex construct, with several different clinical, cognitive and functional correlates. These features represent potential treatment targets to improve the clinical condition and also facilitate the process of recovery and the overall well-being of people living with schizophrenia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".