Evidence-based public health messaging on the non-visual effects of ocular light exposure: a modified Delphi expert consensus
Bibliographic record
Abstract
Introduction: In addition to vision, light regulates circadian rhythms, sleep, mood and alertness. Despite growing scientific understanding, there remains a gap in translating this knowledge into accessible, evidence-based guidance. The goal of this paper is to formulate scientifically grounded statements about the influence of light on human psychological and physiological health, intended for dissemination to the public and policymakers. Methods: An international consortium of 21 experts convened at the Ladenburg Roundtable in April 2024. Experts were selected based on their scientific contributions to chronobiology, psychology, neuroscience, and the measurement and practical application of light. Through a structured, iterative modified Delphi process, 27 statements were developed. Each statement included a simplified public-facing version and contextual information to support understanding. Consensus was assessed using predefined thresholds of agreement (>75% endorsement). Statements not meeting consensus were revised and re-evaluated. Results: Of the 27 proposed statements, 26 reached the threshold for consensus, with high levels of agreement across diverse topics. One statement did not reach consensus due to insufficient scientific evidence and was excluded, while another was revised based on feedback and subsequently accepted. The iterative revision process significantly improved the clarity, accuracy and accessibility of the final statements. A readability assessment showed an average sentence length of 14.8 words and a Flesch-Kincaid Grade Level of 8.6, indicating that the statements suit a broad, non-specialist audience. The final consensus statements are available at lightforpublichealth.org. Conclusions: This expert consensus provides clear, accessible messages about how light affects human health. The statements offer a practical tool for public education and policymaking and can be used by public-health multipliers (eg, schools, employers, healthcare providers and urban planners) to promote healthier light exposure in daily life. They highlight the importance of recognising light as a key factor in health, alongside sleep, nutrition and physical activity.
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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 | 0.234 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".