Boredom proneness predicts quality of life in outpatients diagnosed with schizophrenia-spectrum disorders
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the clinical significance of boredom associated with functional impairments in schizophrenia. Previous work has highlighted the importance of motivational deficits more broadly, although no study has yet explored the unique effects of boredom on community outcomes. AIMS: This study aims to measure boredom proneness among outpatients diagnosed with schizophrenia to determine whether it is elevated in this population and to determine its relation to quality-of-life outcomes. METHODS: A self-report measure of boredom proneness along with standard measures of symptoms and functional status was administered to a community-dwelling sample of schizophrenia outpatients. RESULTS: Boredom proneness was found to be elevated in this population and was associated with reduced quality of life, specifically with leisure activity dissatisfaction and reduced sense of financial well-being. Negative symptoms were determined to be associated with reduced work and school functioning. CONCLUSION: This pattern of unique effects on quality of life highlights the clinical relevance of identifying a subjective state of boredom and has theoretical importance in distinguishing boredom proneness specifically from more general avolitional and amotivational conditions that have tended to be the focus of clinical observation and previous research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".