Relationships between Occupational Factors and Health and Well-Being in Individuals with Persistent Mental Illness Living in the Community
Bibliographic record
Abstract
PURPOSE: This study identified relationships between occupational factors and health and well-being among individuals with persistent mental illness. METHODS: There were 103 subjects assessed in regards to time spent in different occupations, activity level, satisfaction with daily occupations, and experienced occupational value. The health-related variables were self-rated health, quality of life, self-esteem, sense of coherence, self-mastery, psychosocial functioning, and psychiatric symptoms. RESULTS: Subjective perceptions of occupational performance were consistently related to both self-rated and interviewer-rated aspects of health and functioning. While variables pertaining to actual doing showed weak or no associations with self-rated health-related variables, they exhibited moderate relationships to interviewer-rated health and functioning. IMPLICATIONS: The health-promoting ingredients in occupations were determined by the way occupations were perceived, rather than the doing per se. The findings indicate that perceived meaning and satisfaction ought to be prioritized when setting goals in occupational therapy practice, and, besides, that existing occupational therapy theory needs to be updated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".