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Record W2001577936 · doi:10.1155/2014/398791

Effects of Full-Moon Definition on Psychiatric Emergency Department Presentations

2014· article· en· W2001577936 on OpenAlexaff
Varinder S. Parmar, Ewa Talikowska-Szymczak, Emily Downs, P. Szymczak, Erin Meiklejohn, Dianne Groll

Bibliographic record

VenueISRN Emergency Medicine · 2014
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsQueen's University
Fundersnot available
KeywordsFull moonNew moonEmergency departmentMedicineTriageAnxietyPsychiatryMedical emergencyEmergency medicine

Abstract

fetched live from OpenAlex

Objectives . The lunar cycle is believed to be related to psychiatric episodes and emergency department (ED) admissions. This belief is held by both mental health professionals and the general population. Previous studies analyzing the lunar effect have yielded inconsistent results. Methods . ED records from two tertiary care hospitals were used to assess the impact of three different definitions of the full-moon period, commonly found in the literature. The full-moon definitions used in this study were 6 hours before and 6 hours after the full moon (a 12-hour model); 12 hours before and 12 hours after the full moon (a 24-hour model); and 24 hours before and after the day of the full moon (a 3-day model). Results . Different significant results were found for each full-moon model. Significantly fewer patients with anxiety disorders presented during the 12-hour and 24-hour models; however, this was not true of the 3-day model. For the 24-hour model, significantly, more patients presented with a diagnosis of personality disorders. Patients also presented with more urgent triage scores during this period. In the 3-day model, no significant differences were found between the full-moon presentations and the non-full-moon presentations. Conclusions . The discrepancies in the findings of full moon studies may relate to different definitions of “full moon.” The definition of the “full moon” should be standardized for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.031
GPT teacher head0.359
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

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