Lunacy in a tertiary emergency department: A 3‐year cohort study of the association between moon cycles and occupational violence and aggression
Bibliographic record
Abstract
OBJECTIVE: Occupational violence and aggression (OVA) in the ED is an issue of global concern and increasing incidence. The empirical evidence for the relationship between the lunar cycle and 'lunatics' remains equivocal. The present study aims to examine the association between OVA in ED and the full moon (FM). METHODS: Data on all presentations were extracted from The Alfred Hospital ED records for consecutive patients over a 3-year period (January 2013-December 2015). The primary outcome of the present study is OVA among patients in the ED. Univariable and multivariable logistic regression were used to determine the association between aspects of the lunar cycle and OVA. RESULTS: There were 184 059 ED presentations during the 3 years, 6234 (3.4%) of which occurred on a FM. There were 1853 episodes of OVA, 57 (3.1%) of which occurred on a FM. OVA among patients presenting to ED was not associated with the FM (adjusted odds ratio [OR] 0.92 [95% confidence interval 0.70-1.20]; P = 0.53). However, the first quarter (FQ) (adjusted OR 1.38 [1.11-1.72]; P < 0.01) and third quarter (TQ) (adjusted OR 1.29 [95% confidence interval 1.03-1.62]; P = 0.03) moons of the lunar cycle were independently associated with OVA. CONCLUSIONS: Contrary to traditional beliefs, the FQ and TQ of the lunar cycle but not the FM were associated with OVA. This highlights a relatively unexplored relationship that has previously been overshadowed by the FM in the literature. Prediction models of violence in the ED could consider incorporating the FQ and TQ of the lunar cycle in their models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".