Physical health of patients with severe mental illness
Bibliographic record
Abstract
PURPOSE: Patients with severe mental illness (SMI) treated with antipsychotic medication are at increased risk of metabolic side-effects like weight gain, diabetes mellitus and dyslipidaemia. This study aims to examine the feasibility of maintaining a physical health monitoring sheet in patients' records and its impact on physical health of patients with SMI, over a period of one year. DESIGN/METHODOLOGY/APPROACH: A physical health monitoring sheet was introduced in all the patients' records on a 15-bedded male medium secure forensic psychiatric rehabilitation unit, as a prompt to regularly monitor physical health parameters. An audit cycle was completed over a one year period. The data between baseline and re-audit were compared. FINDINGS: At baseline, 80 per cent of the patients were identified as smokers, 80 per cent had increased body mass index (BMI) and 87 per cent had raised cardiovascular risk over the next ten years. Appropriate interventions were offered to address the risks. At re-audit, the physical health monitoring sheets were up to date in 100 per cent of patients' records. The serum lipids and cardiovascular risk over the next ten years reduced over time. No significant change was noted on the parameters including BMI, central obesity, high blood pressure and smoking status. RESEARCH LIMITATIONS/IMPLICATIONS: This was a pilot study and was limited by the small sample size, male gender only and the specific nature of the ward. PRACTICAL IMPLICATIONS: There is a need for improved access to physical health care in long-stay psychiatric settings. A more robust lifestyle modification programme is required to positively influence the physical health parameters in this cohort of patients. ORIGINALITY/VALUE: Introduction of a physical health monitoring sheet in patients' records led to regular screening of cardiovascular risks and subsequent increased prescribing of hypolipidaemic agents in individuals with severe mental illness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".