Sick Building Syndrome Among Junior High School Students in Japan in Relation to the Home and School Environment
Bibliographic record
Abstract
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".