MétaCan
Menu
Back to cohort
Record W2913608008 · doi:10.3390/ijerph16040597

Heat Waves Occurrence and Outdoor Workers’ Self-assessment of Heat Stress in Slovenia and Greece

2019· article· en· W2913608008 on OpenAlexaff
Tjaša Pogačar, Zala Žnidaršič, Lučka Kajfež Bogataj, Andreas D. Flouris, Konstantina Poulianiti, Zalika Črepinšek

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of Ottawa
FundersHorizon 2020 Framework Programme
KeywordsHeat waveHeat stressProductivityEnvironmental healthHeat exhaustionEnvironmental scienceExcess heatMedicineTimelineClimate changePoison controlGeographyAtmospheric sciencesEngineeringEconomic growth

Abstract

fetched live from OpenAlex

Changing patterns of heat waves are part of the global warming effect and the importance of changes is reinforced by their negative impact on society. Firstly, heat waves were analyzed in Brnik (Slovenia) and Larisa (Greece) in the period 1981–2017 to reflect the environment which workers are exposed to. Secondly, outdoor workers (70 from Greece, 216 from Slovenia) provided a self-assessment of heat stress. The heat wave timeline is presented as an effective way of illustrating long-term changes in heat waves’ characteristics for various stakeholders. In both countries, workers assessed as significant the heat stress impact on productivity (Greece 69%, Slovenia 71%; p > 0.05), and in Slovenia also on well-being (74%; p < 0.01). The main experienced symptoms and diseases were thirst (Greece 70%, Slovenia 82%; p = 0.03), excessive sweating (67%, 85%; p = 0.01), exhaustion (51%, 62%; p > 0.05) and headache (44%, 53%; p > 0.05). The most common way to reduce heat stress was drinking more water (Greece 64%, Slovenia 82%; p = 0.001). Among the informed workers, the prevalent source of information was discussions. Therefore, educational campaigns are recommended, together with the testing of the efficiency of mitigation measures that will be proposed on the Heat-Shield project portal.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.080
GPT teacher head0.409
Teacher spread0.329 · 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

Citations41
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicClimate Change and Health ImpactsFrench-language works237,207