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Record W3086294053 · doi:10.5539/gjhs.v12n11p20

A Systematic Review on School Air Quality and Its Impact on Student’s Health in Tropical Countries

2020· review· en· W3086294053 on OpenAlexvenueno aff
Bambang Wispriyono, Budi Hartono, Ririn Arminsih Wulandari, Sasnila Pakpahan, Gita Permata Aryati, Nurmalasari Nurmalasari, Okky Assetya

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersKementerian Riset, Teknologi dan Pendidikan Tinggi
KeywordsAir quality indexEnvironmental healthIndoor air qualityAir pollutantsPollutantParticulatesQuality (philosophy)Air pollutionMedicineEnvironmental scienceGeographyEnvironmental engineeringMeteorology

Abstract

fetched live from OpenAlex

Poor air quality in school areas has a negative impact on the student’s health. Several studies in tropical countries have reported the risk exposure and environmental factors that were associated to the air quality in school areas. This paper presents a review of several case study research associated to air pollutants and environmental factors on the surrounding school environment and the health impact on students in tropical country. We selected and reviewed 18 research papers related to air quality in schools. The selection method was based on the inclusion and exclusion criteria. Throughout these studies, the most common source of air pollutants found in the classroom was particulate matter. Air quality in schools is affected by the distance between the school and the source of pollutants, ventilation, inhabitant, and season. Exposure to poor indoor air quality can increase health risk, respiratory problems, ocular problems, and students’ absence from school.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.107
GPT teacher head0.502
Teacher spread0.395 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2020
Admission routes1
Has abstractyes

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