MétaCan
Menu
Back to cohort
Record W4214627565 · doi:10.4236/health.2022.142020

Occupational Heat Stress in the Floriculture Industry of Ethiopia: Health Risks and Productivity Losses

2022· article· en· W4214627565 on OpenAlexfundno aff
Belay Simane, Abera Kumie, Kiros Berhane, Jonathan M. Samet, Tord Kjellström, Jonathan A. Patz

Bibliographic record

VenueHealth · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesInternational Development Research CentreFogarty International CenterNational Institutes of HealthCenters for Disease Control and PreventionNational Institute for Occupational Safety and HealthGeneral Administration of Customs
KeywordsWet-bulb globe temperatureEnvironmental healthHeat stressProductivityGreenhouseEnvironmental scienceMedicineEconomicsAgronomyEconomic growth

Abstract

fetched live from OpenAlex

Background: The Ethiopian flower industry is growing fast with successful diversification of export products under greenhouse structures. Higher temperatures in the greenhouses pose a serious threat to the health of workers and add to the risk of occupational heat stress. Excessive heat in workplace settings also reduces work capacity and labour productivity. This study aims to investigate the level of heat exposure, and workers’ and managers’ perceptions and behavioural responses towards extreme heat exposure in a warming climate. Methods: We used the Wet Bulb Globe Temperature (WBGT) measured in representative greenhouses to capture the heat exposure during hotter and cooler seasons following ISO 7243 (generally risk of heat stress occurs when WBGT exceeds 26°C). A comparative cross-sectional study design with a stratified sampling method was used to assess occupational heat stress and workers’ perceptions of the impact of heat on their health and productivity in six different floriculture greenhouses in Ethiopia representing three different agro-ecologies and products. A questionnaire survey was conducted (30 managers/supervisors and 305 workers; 76.1% female) to capture perceptions on heat exposures, symptoms of potential health impacts, productivity losses and coping mechanisms. Results: Heat exposure varied across different agroecologies, product types and greenhouse materials with a median WBGT Index of 25.5°C and a range from 18.1°C to 31.5°C. The impact of heat stress also varied across different employment sectors and geographical regions. Overall, workers in cut-flower greenhouses are exposed to higher than recommended WBGT Index (26°C) for 3 - 6 working hours daily. 65% of the managers reported that heat stress has a significant impact on the workers’ labour productivity, but do not have guidance about working in hot conditions. Workers reported more heat-related health issues and reduced productivity, especially in the mid-altitude greenhouses. About 50% of the workers reported that heat exposure decreased work productivity during hot hours. Sweating, exhaustion, heat-rashes, dehydration, crumps, nausea and headache were self-reported health issues. Labour productivity losses ranged from no loss to 19.5% in the mid- and low-altitudes. Conclusions and Recommendations: Excessive workplace heat in the greenhouses is both an occupational health hazard and detrimental to productivity in the floriculture industry. However, the level of understanding and actions on the ground regarding occupational heat stress are low. The code of conduct in place now does not consider the occupational heat stress issues. Multiple actions (engineering, management, training and policy-related recommendations) have to be implemented by Ethiopian Horticultural Producers and Exporters Association (EHPEA) and farm owners to mitigate heat stress and loss of productivity. Designing and implementing these heat prevention strategies and incorporating them into the code of conduct is in the interests of both employers and employees.

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)0.001
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 score0.051
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.148
GPT teacher head0.391
Teacher spread0.243 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueHealthSame topicClimate Change and Health ImpactsFrench-language works237,207