An Outcome in Need of Clarity: Building a Predictive Model of Subjective Quality of Life for Persons With Severe Mental Illness Living in the Community
Bibliographic record
Abstract
PURPOSE: The study purpose was to construct a predictive model of subjective quality of life for persons with severe mental illness living in the community with particular attention to participation in occupations. METHOD: Persons with severe mental illness (N=154) rated their subjective quality of life. Several measures for each of the following categories of variables were completed: demographics, clinical, social participation, and self-measured well-being. Regression analysis was used to determine the significant predictors for each category and then to build the predictive model from these significant variables. RESULTS: Symptom distress accounted for the most variance (33%) in subjective quality of life, followed by psychological integration (3%) and physical integration (2%). CONCLUSIONS: The study suggests that occupational therapists should attend to subjective experience of symptoms to influence quality of life. Therapists are also in a good position to address their clients' sense of belonging to their communities and to enable community participation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".