65 What to wear? Occupational hygiene applications for wearables
Bibliographic record
Abstract
Abstract Evaluation of exposure in occupational hygiene usually means monitoring the environment in which workers work and then comparing those measurements to established exposure limits. This one size-fits-all approach is protective of the majority of a healthy working population but does not account for variability in how individual workers react or metabolize certain hazards. Wearable technologies continue to evolve and proliferate, particularly in the workplace, being used to monitor a broad range of employee performance and health metrics in equally diverse use case environments. These technologies afford continuous monitoring of individual-specific data-driven outcomes, enabling more targeted risk mitigations and interventions to be developed, implemented, and evaluated. Wearable devices that can detect, measure, and compare a user’s heart rate, sweat output and composition, fatigue, sleep quality, etc., for example, would allow occupational hygienists to monitor and remedy, where possible, individual effects of exposure, both acutely and longitudinally, in a way that hanging an environmental monitor on a worker, simply cannot. Moreover, these data can be aggregated and visualized at the employer-level, such that targeted strategies for enhancing employee performance and health can be developed, evaluated, and refined as needed. Additionally, they provide a means through which complementary employee-specific insights and guidance can be provided to enhance compliance. This round table session will discuss the potential for wearables integration for occupational exposure evaluations, overarching research, testing, and evaluation approaches to inform optimal fit-for-purpose solutions, and thus the utility, challenges, and opportunities for using targeted wearables for occupational exposure purposes moving forward.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.038 |
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".