Is a JEM an informative exposure assessment tool for night shift work?
Bibliographic record
Abstract
In this issue of Occupational and Environmental Medicine , Fernandez et al 1 investigate the role of maternal night shift work in occurrence of urogenital anomalies in offspring. The authors inferred potential exposure to night shift work by applying a job-exposure matrix (JEM)2 to mothers’ recorded occupations in the Australian Perinatal Registry. This study’s assessment of night shift work across various occupations, with nurses reported separately from other types of workers, adds valuable knowledge on a rarely studied outcome. The authors acknowledge that the lack of individual-level information on shift schedules precluded their ability to assess differences in duration and/or intensity of night shift work, which may variably interfere with reproductive function and recommend ‘investigation in a sample with more detailed exposure information’. This is an important recommendation given the widely recognised complexity of assessing exposures in epidemiological studies of night shift work.3 ‘Night shift work’ refers to work that occurs during the regular sleeping hours of the general population.3 As such, it is not an exposure in the traditional sense, but rather a proxy for a complex combination of exposures and circumstances leading to circadian disruption.4 In addition to a wide variety of schedule characteristics, these include light at night (LAN), phase shift, sleep disturbances, disrupted social behaviours and personal habits and other workplace hazards, many of which are strongly inter-related. Moreover, personal characteristics …
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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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".