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Record W1977369773 · doi:10.2190/ns.22.4.d

Chemical Exposures of Women Workers in the Plastics Industry with Particular Reference to Breast Cancer and Reproductive Hazards

2012· review· en· W1977369773 on OpenAlexaff
Robert DeMatteo, Margaret M. Keith, James T. Brophy, Anne Wordsworth, Andrew Watterson, Matthias Beck, Anne Rochon Ford, Michael Gilbertson, Jyoti Pharityal, Magali Rootham, Dayna Nadine Scott

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsMcGill UniversityYork UniversityUniversity of WindsorCanada Auto Workers
Fundersnot available
KeywordsEnvironmental healthOccupational hygieneWorkforceOccupational exposureMedicineBreast cancerWork (physics)BusinessOccupational safety and healthCancerEngineeringPolitical sciencePathology

Abstract

fetched live from OpenAlex

Despite concern about the harmful effects of substances contained in various plastic consumer products, little attention has focused on the more heavily exposed women working in the plastics industry. Through a review of the toxicology, industrial hygiene, and epidemiology literatures in conjunction with qualitative research, this article explores occupational exposures in producing plastics and health risks to workers, particularly women, who make up a large part of the workforce. The review demonstrates that workers are exposed to chemicals that have been identified as mammary carcinogens and endocrine disrupting chemicals, and that the work environment is heavily contaminated with dust and fumes. Consequently, plastics workers have a body burden that far exceeds that found in the general public. The nature of these exposures in the plastics industry places women at disproportionate risk, underlining the importance of gender. Measures for eliminating these exposures and the need for regulatory action are discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score0.520

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 integrity0.0000.000
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 head0.049
GPT teacher head0.335
Teacher spread0.286 · 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 designOther design
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

Citations68
Published2012
Admission routes1
Has abstractyes

Explore more

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