Review of ethics for occupational hygiene hazard monitoring surveys using sensors
Bibliographic record
Abstract
This review is about the ethical use of sensors to monitor occupational exposure to hazards. It considers whether the same or different, ethical measures apply to using sensors, compared to conventional hazard monitoring surveys. To undertake the review, subject experts developed a research question, identified suitable search terms, and set the scope of these searches. Candidate research papers dating from 2000 to mid-2022 that met inclusion criteria were identified and reviewed by each author. Ethical concerns were identified by the authors of studies in which sensors were used to monitor employee health and well-being, but most of the studies that used them to monitor employee exposure to hazards focused on the technical aspects of their deployment. These ethical concerns included questions about employee rights and privacy, the anonymity of the data collected with sensors, and how the security of this data is managed. The review considers ethical standards and codes of practice for occupational hygiene work and the ethical risks when sensors are used to gather data. Sensors may provide insight into occupational exposure to hazards, but their use is not always adequately explained to employees by those managing this monitoring work. The ethical concerns identified were relevant to many areas of industrial hygiene work, but more studies are required that consider the ethical use of sensors in workplaces. Studies that monitored employee health, well-being, and productivity, identified ethical risks in using sensors to monitor these endpoints. An ethical framework and checklist for hygienists are proposed including a set of questions that consider the risks of using sensors to monitor occupational hazards. Industrial hygiene professional bodies provide ethical codes of practice for their members but may also need to consider the implications of using sensors in workplaces. Ethical standards support the collection of industrial hygiene exposure data whilst maintaining the privacy rights of employees.
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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 | 0.206 | 0.581 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".