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Record W4416134678 · doi:10.1177/0734242x251385835

Assessment of occupational hazards facing waste pickers to support a proper closure of a large open dump

2025· article· en· W4416134678 on OpenAlexaff
Greice Kelly Menezes Martins, Hayssa Moraes Pintel Ramos, Morteza Bashash, Tara Rava Zolnikov, Jutta Gutberlet, Dayani Galato, Vanessa Resende Nogueira Cruvinel

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of Victoria
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversidade de Brasília
KeywordsWork (physics)Occupational safety and healthClosure (psychology)Municipal solid wasteHuman healthBiological hazardPersonal protective equipmentHazard

Abstract

fetched live from OpenAlex

Waste pickers are workers exposed to several occupational risks. This cross-sectional study investigated hazards associated with occupational accidents among waste pickers in the largest open dumpsite in Latin America before its closure. A survey was conducted with 999 waste pickers, collecting data on accidents and work characteristics. Among those interviewed, 686 (68.7%) of waste pickers reported having suffered accidents at work. Of these, 68.7% of them were related to exposure to sharp objects, and 62.5% were unable to work due to the accident. Work shift and work environment were significantly associated with accidents ( p < 0.001). It indicates that workplace hazards are stronger predictors of accidents than personal characteristics. This article demonstrates the necessity for integrated and multi-sectoral strategies and surveillance systems that monitor environmental, animal and human health indicators aligned with the One Health approach in municipal solid waste management. This epidemiological study provides data to support dump closure in similar contexts, especially in countries in the Global South.

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)0.015
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.006
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 head0.045
GPT teacher head0.400
Teacher spread0.356 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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Same venueWaste Management & Research The Journal for a Sustainable Circular EconomySame topicMunicipal Solid Waste ManagementFrench-language works237,207