O06-6 Quantifying shiftworkers’ exposure to light-at-night in healthcare and emergency service settings
Bibliographic record
Abstract
Background Shiftworkers’ increased risk of adverse health outcomes has been linked to their exposure to light-at-night (LAN). However, few data characterising light exposures in nighttime working environments have been reported. The purpose of this study was to measure and compare shiftworkers’ exposures to LAN in the province of British Columbia, Canada. Methods Between October 2015 and March 2016, over 150 personal full-shift data were collected from more than 100 shiftworkers in emergency health services (paramedics, dispatchers) and healthcare (nurses, support aides, security, pharmacy, laboratory staff). Participants wore a small continuous monitoring device to measure LAN exposure once per minute. Preliminary analyses during the 23:00–05:00 period were conducted to characterise exposure levels with potential impacts on melatonin secretion and alertness. Results Personal light exposure levels ranged from <5 lux (minimum) to >650 lux (maximum). Cumulative time ≥1 hour above 30 lux (minimum level influencing melatonin secretion) occurred in over 60% of samples, and in over 40% of samples using a 100 lux cut point (midpoint of light’s maximum alerting effect). Significant exposure differences were seen across occupations and workplaces. Nurses, aides, and laboratory workers had the longest cumulative time exceeding 100 lux; dispatchers had the longest cumulative time below 30 lux. By workplace, laboratory, intensive care, and labour and delivery workers had the longest cumulative time exceeding 100 lux; call centre workers had the longest cumulative time below 30 lux. Within-worker variation across shifts was most pronounced among laboratory assistants and least pronounced among dispatch officers. Conclusions This study constitutes an initial step in documenting and comparing levels of LAN exposure across shiftwork occupations, workplaces, and time. Results will be useful for planning future LAN sampling strategies, assigning exposure estimates in epidemiological analyses, and identifying groups of shiftworkers at greatest risk of circadian disruption and/or fatigue at work.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".