Predictors of quality of life among inpatients in forensic mental health: implications for occupational therapists
Bibliographic record
Abstract
Optimising quality of life (QOL) for service users in a forensic hospital is an important treatment objective. The factors which contribute to QOL in this setting are currently unclear. The aim of this study was to analyse the predictors of QOL amongst service users within an inpatient forensic mental health hospital. This study is a naturalistic, cross-sectional, observational study. Fifty-two male service users with schizophrenia or schizoaffective disorder participated in the study. QOL was measured using the World Health Organisation QOL Bref. We used the Engagement in Meaningful Activity Survey (EMAS), ward atmosphere was measured using the Essen Climate Evaluation Schema (EssenCES), occupational functioning was assessed using the Social and Occupational Functioning Scale (SOFAS). We also collected level of ward security, length of stay and community leave data. Stepwise regression showed that meaningful activity, level of ward security, and therapeutic hold on the EssenCES significantly predicted QOL on a range of specific QOL domains. These variables accounted for 40% of the variance for total QOL score. Engagement in meaningful activity added the largest contribution to total QOL score accounting for 30% of the variance. This study shows that provision of meaningful activities, level of ward security and therapeutic hold may contribute to QOL amongst forensic mental health inpatients.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".