0980 Circadian Patterns of Aggressive Behaviors in a Mental Health Care Facility
Bibliographic record
Abstract
Abstract Introduction There are indications that circadian rhythms regulate certain aspects of emotions and behaviors. Yet, little is known about the potential diurnal rhythm of behavioral problems in individuals at risk for both circadian and emotional deregulation. This study investigates whether the frequency of aggressive behaviors among individuals receiving mental health care in a tertiary psychiatric facility follows a circadian pattern. Methods The timing of all “code white” alerts, emergency notifications of aggressive behavior, were documented from the hospital occupational safety team during 2022 and collated for secondary data analysis. A repeated measures ANOVA was performed on the hourly frequency of code white alerts across 24-hours and a Fourier series model was fitted to the data to extract parameters of a putative circadian curve. Results Preliminary results reveal a significant time of day effect on code white alerts (F(23, 8349) = 9.58, p <.001). Visual inspection show a sinusoidal pattern in the hourly counts of code white alerts, with an acrophase between 2 PM and 3 PM and a nadir between 3AM and 4AM. This was confirmed by the curve fitting (Adjusted R-square =.91). Conclusion These initial findings suggest a circadian modulation in the occurrence of aggressive behaviours in people receiving mental health care. While further work is required to understand underlying mechanisms, this phenomenon may be linked to the decrease in alertness and energy levels in the afternoon, which may make emotional regulation and decision-making more challenging. Better understanding of the influence of circadian factors on aggressive behaviors may facilitate self-regulation strategies and guide healthcare teams in preventing and better tailoring their responses to behavioral emergencies. Support (if any) NA
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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 | 0.000 | 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".