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Record W4320028279 · doi:10.1134/s0001433822070088

The Influence of Moon Phases on the Frequency of Admissions to a Psychiatric Hospital

2022· article· en· W4320028279 on OpenAlexaboutno aff
K. A. Maslov

Bibliographic record

VenueIzvestiya Atmospheric and Oceanic Physics · 2022
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Full moonMental hospitalPsychiatryNew moonMedicinePsychologyGeographyAstronomyPhysics

Abstract

fetched live from OpenAlex

Abstract The influence of the moon phase on persons with mental disorders and the frequency of emergency and urgent admissions to the mental hospital in the city of Penza and Penza oblast (Russia) in 2018–2019 has been investigated. The frequency of hospital admissions is estimated among persons with severe mental disorders (with pronounced psychotic and pronounced affective and behavioral manifestations) during the periods of different moon phases: the full moon, new moon, first quarter, and last quarter. According to the results of the study, it has been found that the frequency of admissions did not differ significantly in the different moon phases (p = 0.4382), which agrees with the data of similar international studies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.351

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)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.276
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2022
Admission routes1
Has abstractyes

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