Wearable Biosensors in the Workplace: Perceptions and Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: Wearable body and brain sensors are permeating the consumer market and are increasingly being considered for workplace applications with the goal of promoting safety, productivity, health, and wellness. However, the monitoring of physiologic signals in real-time prompts concerns about benefit and risk, ownership of such digital data, data transfer privacy, and the discovery and disclosure of signals of possible health significance. Here we explore the perceptions and perspectives of employers and employees about key ethical considerations regarding the potential use of sensors in the workplace. Methods: We distributed a survey developed and refined based on key research questions and past literature to a wide range and size of industries in British Columbia, Canada. Both employers (potential Implementers) and employees (potential Users) were invited to participate. Results: We received 344 survey responses. Most responses were from construction, healthcare, education, government, and utilities sectors. Across genders, industries, and workplace sizes, we found a convergence of opinions on perceived benefit and concern between potential Implementers and potential Users regarding the motivation to use biosensors in the workplace. Potential Implementers and Users also agreed on issues pertaining to safety, privacy, disclosure of findings of possible medical significance, risks, data ownership, data sharing, and transfer of data between workplaces. The greatest variability between potential Users and Implementers pertained to data ownership. Conclusion: Strong agreement in the perception of biosensor use in the workplace between potential Implementers and Users reflects shared interest, motivation, and responsibility for their use. The use of sensors is rapidly increasing, and transparency about key use factors-both practical and ethical-is essential to maintain the current and desirable level of solidarity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it