Social, Demographic, and Clinical Factors Related to Disruptive Behaviour in Hospital
Bibliographic record
Abstract
OBJECTIVE: This study addresses 2 issues: first, whether the diagnosis of borderline personality disorder (BPD) or borderline traits differentiates adult patients who demonstrate disruptive behaviour during hospitalization from those patients who do not; and second, whether other clinical variables can be assessed during the emergency visit to differentiate patients who are likely to show disruptive behaviour in hospital from those who are not. METHOD: We completed a case-control, chart-based, retrospective analysis of patients consecutively admitted to an inpatient psychiatric service. We assembled 44 subjects who demonstrated evidence of disruptive behaviour during inpatient hospitalization. These subjects were matched with 61 control subjects admitted during the same time period. Potential participants were excluded if they had a diagnosis of schizophrenia, psychotic disorders, delirium, or dementia or if they had a diagnosis receiving a psychotic specifier. RESULTS: Univariate analyses revealed that patients with disruptive behaviour were significantly more likely to have been diagnosed with BPD or borderline traits than the comparison group (32.6% vs 13.4%; chi2 = 4.45, df 1; P < 0.05). According to stepwise logistic regression analysis, 4 variables significantly contributed to the final model (R2 = 0.37, P < 0.001) predicting disruptive behaviours with the following odds ratios (ORs): Axis III infectious diseases (OR 7.63; 95% CI, 1.41 to 41.67), Axis IV housing problems (OR 3.58; 95% CI, 1.21 to 10.64), history of suicidal behaviours (OR 3.46; 95% CI, 1.24 to 9.71), and problems with primary supports (OR 0.12; 95% CI, 0.03 to 0.46). This last variable was related to a reduced risk of disruptive behaviours in hospital. CONCLUSIONS: Patients at risk for disruptive behaviour during psychiatric hospitalization are characterized by a history of suicidal or impulsive-aggressive behaviour and social disadvantage.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".