Is it the moon? Effects of the lunar cycle on psychiatric admissions, discharges and length of stay
Bibliographic record
Abstract
BACKGROUND: There is an ongoing debate concerning the connection between lunar cycle and psychiatric illness. AIMS OF THE STUDY: The purpose of the present study was to evaluate the rates of admission to and discharge from psychiatric inpatient treatment, as well as the length of stay, in relation to the lunar cycle, including 20 different categories of phases of the moon. METHODS: The data of 17,966 cases of people treated in an inpatient setting were analysed. Routine clinical data and data about admission and discharge were used. The lunar calendar was obtained from the website of the US Naval Observatory and was used to calculate the dates of the full moon according to the geographic location of the clinics. The clinics are located in the Canton Grisons in Switzerland. The following phases of the moon throughout the lunar cycle were defined: (a) full moon, (b) quarter waxing moon, (c) new moon, and (d) quarter waning moon. In addition, we coded one day and two days preceding every lunar phase as well as the two days following the respective phases of the moon. RESULTS: The lunar cycles showed no connection with either admission or discharge rates of psychiatric inpatients, nor was there a relationship with the length of stay. CONCLUSIONS: Despite the widespread belief that the moon impacts peoples’ mental health and subsequently psychiatric treatment, this study provides no evidence that our celestial neighbour influences our mental well-being.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".