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Record W1565829205 · doi:10.5539/gjhs.v8n2p165

Sick Building Syndrome Among Junior High School Students in Japan in Relation to the Home and School Environment

2015· article· en· W1565829205 on OpenAlexvenueno aff
Motoko Takaoka, Kyoko Suzuki, Dan Norbäck

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsnot available
Fundersnot available
KeywordsSick building syndromeMedicineAtopyLogistic regressionAllergyRelative humidityEnvironmental healthPediatricsDemographyGeographyIndoor air qualityInternal medicineImmunologyMeteorology

Abstract

fetched live from OpenAlex

PURPOSE: There is an increasing concern about sick building syndrome (SBS), especially in Asia. The aim of this study is to investigate associations between SBS and the home, school environment and personal factors among Japanese junior high school students. METHODS: We investigated students in four junior high schools in Hyogo in Kansai area, Japan. A questionnaire study was performed among students (n=1056), 12-15 years old. Temperature and relative air humidity was measured in the classrooms and dust was collected from the classroom floors and air and was analysed for cat and dog allergens. Associations were analysed by multi-level logistic regression. RESULTS: Mucosal symptoms (45.4%), general symptoms (38.9%) and skin symptoms (22.6%) were common. Totally 8.8% reported cat allergy, 6.1% dog allergy, 6.0% mold allergy and 25.7% pollen allergy. Atopy, window pane condensation, floor dampness and odor at home and high relative air humidity in the classrooms were associated with SBS. CONCLUSION: The prevalence of SBS symptoms was high and associated with both home and school environment. Window pane condensation and floor dampness at home can increase the risk for SBS symptoms in students. Moreover high relative air humidity at school may increase the risk for SBS.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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 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

Citations52
Published2015
Admission routes1
Has abstractyes

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