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Record W4412046303 · doi:10.1177/00333549251342205

Effect of Heat on Outdoor Recess in Arizona Elementary Schools, July–September 2023

2025· article· en· W4412046303 on OpenAlexaboutno aff
Allison Ross, Kylie Wilson

Bibliographic record

VenuePublic Health Reports · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)MedicineExtreme heatDemographyGerontologyEnvironmental healthGeographyClimate changeSociologyEcology

Abstract

fetched live from OpenAlex

School recess is an important setting to support children's health; however, inclement weather, including extreme heat, affects the provision of recess. We conducted a cross-sectional study to determine the number of days that recess in elementary schools was disrupted because of heat in the first quarter of the 2023-2024 school year. We obtained data by conducting a survey among teachers representing 61 elementary schools in Maricopa County, Arizona. Daily high temperatures during this time ranged from 90°F (32.2°C) to 119°F (48.3°C). Almost all schools (93%) modified traditional outdoor recess because of heat. Across schools, recess disruption averaged 3.5 weeks. Almost half of the schools (49%) reported 4 to 6 weeks of disrupted recess, comprising 44% to 67% of the first quarter of the school year or up to 16% of the entire school year. Although state policies mandating daily school recess exist, they generally do not address the provision of recess during inclement weather, including heat. Given the health benefits of recess for children, minimizing heat exposure in schoolyards and supporting indoor recess during times of extreme heat should be prioritized.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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)0.0010.000

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 head0.043
GPT teacher head0.373
Teacher spread0.330 · 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 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

Citations4
Published2025
Admission routes1
Has abstractyes

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