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Record W2124508333 · doi:10.1177/070674370304800909

Barometric Pressure, Emergency Psychiatric Visits, and Violent Acts

2003· article· en· W2124508333 on OpenAlexvenueno aff
Thomas J Schory, Natasha Piecznski, Sunil Nair, Rif S. El‐Mallakh

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2003
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsPsychiatryMetropolitan areaMedicinePoison controlInjury preventionSuicide preventionOccupational safety and healthEmergency departmentHuman factors and ergonomicsIncidence (geometry)DecompensationMedical emergencyEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Associations between human behaviour and psychiatric decompensation and weather variables have been inconsistent. OBJECTIVES: We studied the association of certain weather variables (specifically, humidity, wind speed, and barometric pressure) with emergent psychiatric presentations, psychiatric admissions, incidence of violent crimes, and suicides in a metropolitan area. METHOD: We performed a retrospective study for the year 1999 in a mid-sized city. We included all documented emergent psychiatric visits to the city's psychiatric emergency room. We obtained violence data from the city police department and suicide data from the country medical examiner. RESULTS: The data suggest that total numbers of acts of violence and emergency psychiatry visits are significantly associated with low barometric pressure. Psychiatric inpatient admissions and suicides are not associated with any of the weather variables investigated. CONCLUSIONS: While alternate conclusions can be drawn, we propose that the data support the interpretation that low barometric pressure is associated with an increase in impulsive behaviours. Additional investigation is warranted.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.277
Teacher spread0.259 · 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 designNot applicable
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

Citations76
Published2003
Admission routes1
Has abstractyes

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