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Record W2519846488 · doi:10.1136/oemed-2016-103904

Shift work practices and opportunities for intervention

2016· article· en· W2519846488 on OpenAlexaboutno aff
Kyriaki Papantoniou, Céline Vetter, Eva Schernhammer

Bibliographic record

VenueOccupational and Environmental Medicine · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsnot available
Fundersnot available
KeywordsShift workMedicineIntervention (counseling)Work (physics)Breast cancerDiseaseOccupational cancerOccupational diseaseEnvironmental healthOccupational safety and healthPopulationWorkers' compensationGerontologyCompensation (psychology)CancerOccupational exposurePsychologyNursingPathologyEngineering

Abstract

fetched live from OpenAlex

There is increasing evidence that shift work, an occupational exposure affecting about one-fourth of the working population, increases the risk of major chronic disease outcomes, such as cardiovascular disease and cancer.1–4 Currently, there is an open discussion on whether shift work should be included in national lists of occupational hazards for compensation purposes. Denmark was the first (and to date only) country to consider breast cancer an occupational disease in shift workers, and to compensate women with over 20 years of night work who developed breast cancer. Chronic disease risk reduction and prevention in shift workers is an emerging field, which points to the need for more intervention studies. Whether and how companies or governments translate existing evidence into real-world policy or preventive actions currently remains largely unknown. The study by Hall et al 5 is a unique effort and first step to investigate the extent to which companies from across occupational sectors in the Canadian province of British Columbia implement programmes with potential health impact for their employees. In …

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.091
GPT teacher head0.338
Teacher spread0.247 · 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.

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

Citations18
Published2016
Admission routes1
Has abstractyes

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