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Record W1958893649 · doi:10.1002/ajim.22424

The Inclusion of Women in Studies of Occupational Cancer: A Review of the Epidemiologic Literature From 1991–2009

2015· review· en· W1958893649 on OpenAlexaff
Karin Hohenadel, P.M. Raj, Paul A. Demers, Shelia Hoar Zahm, Aaron Blair

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2015
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsPublic Health OntarioUniversity of TorontoOccupational Cancer Research Centre
FundersU.S. Department of Labor
KeywordsMedicineCancerGerontologyInclusion (mineral)DemographyEpidemiologyOccupational medicineOccupational exposureEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Since the early 1990s, researchers have been concerned with the low rate at which women are included in epidemiologic studies of occupational cancer. A previous evaluation determined that one-third of articles published between 1970 and 1990 included women. METHODS: To assess whether there has been an improvement in recent years, papers on occupational cancer between 1991 and 2009 were reviewed in fifteen journals. RESULTS: The proportion of articles that included men remained stable around 90%, while the proportion of articles that included women increased substantially, from 39% in 1991-1995 to 62% in 2006-2009. Articles that assessed risk among men only or men and women presented a higher number of risk estimates and were more likely to evaluate dose-response relationships than studies including women. CONCLUSIONS: Despite advances in the inclusion of women in studies of occupational cancer, disparities remain in the number of studies of occupational cancer and depth of analysis in studies that included women.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.977
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.156
GPT teacher head0.444
Teacher spread0.288 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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