The Prevalence and Factors Associated With Antipsychotic Polypharmacy in a Forensic Psychiatric Sample
Bibliographic record
Abstract
Despite clinical guidelines limiting the use of multiple concomitant antipsychotics to the most exceptional and treatment resistant cases, the prevalence of antipsychotic polypharmacy has been increasing worldwide. There has been minimal research investigating the prevalence of antipsychotic polypharmacy in forensic psychiatric samples and the correlates associated with antipsychotic polypharmacy. This cross-sectional study aimed to establish the prevalence of antipsychotic polypharmacy in a forensic psychiatric inpatient sample and to investigate the demographical, clinical, and forensic factors associated with polypharmacy. All patients (N = 142) were prescribed at least one antipsychotic at the time of the study. Antipsychotic polypharmacy was prescribed to 54.93% of patients. Logistic regression results indicated increased length of hospitalization, high/medium security level, treatment with clozapine, and depot antipsychotic prescription were predictive of being placed on an antipsychotic polypharmacy regimen. The results suggest that those who are prescribed multiple antipsychotics are long stay patients who present with higher clinical complexity. The results from this study can be used to inform clinical practice leaders about the prevalence of antipsychotic polypharmacy in a forensic psychiatric institution. More research is needed to understand the clinical justifications for prescribing multiple antipsychotics in a forensic psychiatric sample and ways to safely reduce the prevalence of antipsychotic polypharmacy.
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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 | 0.005 |
| 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 | 0.001 |
| 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".