Occupational performance and satisfaction of individuals with mental disorders in Jordan: A cross-sectional study
Bibliographic record
Abstract
Introduction: Individuals with mental disorders face challenges while performing occupations. Existing evidence is limited to Western countries and certain daily life occupations that do not explore all factors related to occupational performance. This study aimed to explore occupational performance and satisfaction in individuals with mental disorders, explore the challenging occupations, investigate the relationship between demographic characteristics to occupational performance and satisfaction, and investigate the factors affecting the occupational performance of individuals with mental disorders. Methods: This was a descriptive correlational cross-sectional study that used the convenience sampling method. It included 95 individuals with mental disorders that had a mean age of (34.46 ± 12.22) years, and were from different mental health care facilities. Additionally, this study used the Canadian Occupational Performance Measure instrument. Results: Individuals with mental disorders had a low mean occupational performance and satisfaction scores (5.8 ± 1.7), (5.2 ± 2.0), respectively. Instrumental Activities of Daily Living were the most reported challenging occupations. There was a relationship between work status and both occupational performance and satisfaction (r = 0.243, p = 0.018), (r = 0.239, p = 0.020), respectively. Also, there was a relationship between the diagnosis and occupational performance (r =0.219, p = 0.033), and work status predicted occupational performance (α ≤ 0.05). Work status is a predictor of occupational performance in individuals with mental disorders.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".