Crowding and Violence on Psychiatric Wards: Explanatory Models
Bibliographic record
Abstract
Objective: Violence is widely prevalent on acute-care psychiatric wards, and crowding has been identified as a major risk factor. This paper explores why patients may respond to crowding with violence. Method: We carried out a literature review on Medline, using the key words “violence” and “crowding.” We conducted an additional hand search of the references collected from the reviewed papers. Results: Factors specific to the relation between crowding on acute-care inpatient psychiatric wards and violence can be divided under the following headings: 1) patient density, privacy, and control; 2) ward architecture; 3) the social organization of psychiatric wards; 4) interpersonal space; 5) phylogenic theories; and 6) anthropological theories of human behaviour. Conclusions: We offer explanatory models for this relation and suggest strategies to counter the effects of crowding. Recommendations are made for future studies. Objectif: La violence dans les unités de soins psychiatriques aigus est très répandue et l'on a reconnu que le surpeuplement est un important facteur de risque. Le présent article cherche à savoir pourquoi les patients peuvent répondre au surpeuplement par la violence. Méthode: Une revue de la documentation a été menée dans Medline à l'aide des mots clés violence et surpeuplement. Une recherche manuelle additionnelle des références recueillies dans les articles examinés a aussi été menée. Résultats: Les facteurs spécifiques de la relation entre le surpeuplement des unités de soins psychiatriques aigus et la violence peuvent se diviser selon les catégories suivantes: 1) la densité, l'intimité et le contrôle des patients, 2) l'architecture de l'unité, 3) l'organisation sociale des unités psychiatriques, 4) l'espace interpersonnel, 5) les théories phylogéniques et 6) les théories anthropologiques du comportement humain. Conclusions: Les modèles explicatifs de cette relation sont offerts et des stratégies sont suggérées pour contrer les effets du surpeuplement. Des recommandations sont faites pour de futures études.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".