Working Time Arrangements as Potential Risk Factors for Ischemic Heart Disease Among Workers in Denmark: A Study Protocol
Bibliographic record
Abstract
BACKGROUND: It has long been suspected that a worker's risk of developing an ischemic heart disease (IHD) may be influenced by his or her working time arrangements. A multitude of studies have been performed, and special attention has been given to long working hours and nighttime work. The statistical powers of the individual studies have, however, generally been too low to either dismiss or confirm an actual relationship, and meta-analyses of underpowered studies are generally associated with publication bias. Hence, uncertainty remains and whether these factors indeed are related to IHD has yet to be settled. OBJECTIVE: This project will test whether the incidences of IHD and usage of antihypertensive drugs among employees in Denmark are independent of weekly working hours and nighttime work. The objective of this paper is to present the intended analyses. METHODS: We will link individual participant data from the Danish labor force survey, 1999-2013, to data on socioeconomic status, industry, emigrations, redeemed prescriptions, hospitalizations, and deaths from registers covering the entire population of Denmark. The study will include approximately 160,000 participants, who will be followed through the registers, from the time of the interview until the end of 2014, for first occurrence of IHD and for antihypertensive drug treatment. We will use Poisson regression to analyze incidence rates as a function of nighttime work and of weekly working hours. RESULTS: We expect results to be ready in mid-2017. CONCLUSIONS: To our knowledge, this will be the largest study ever of its kind. It will, moreover, be free from hindsight bias, since the hypotheses, inclusion criteria, significance levels, and statistical models will be completely defined and published before we are allowed to link the exposure data to the outcome data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".