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The Impact of Occupational Shift Work and Working Hours During Pregnancy on Health Outcomes: A Systematic Review and Meta-analysis

2020· review· en· W3080261053 on OpenAlexaff
Chao Cai, Ben Vandermeer, Rshmi Khurana, Kara Nerenberg, Robin Featherstone, Meghan Sebastianski, Margie H. Davenport

Bibliographic record

VenueObstetric Anesthesia Digest · 2020
Typereview
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsMedicineWorkforcePregnancyShift workMeta-analysisWorking hoursObstetricsWork (physics)Work hoursPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

(Am J Obstet Gynecol. 2019;221:563–576) Women make up a significant proportion of the workforce, and most work during their pregnancies. While studies have found an association between adverse pregnancy outcomes and various working hours, such as shift work or working longer than the standard 40-hour work week, the results have been conflicting. The aim of this study was to assess the impact of shift work and long working hours during pregnancy on maternal and fetal outcomes, and to determine if there is a relationship between the length of working hours and these outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.019
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.390
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations27
Published2020
Admission routes1
Has abstractyes

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