To track or not to track? Employees’ data privacy in the age of corporate wellness, mobile health, and GDPR†
Bibliographic record
Abstract
... The latest digital health developments have allowed for a better tracking of individuals’ health through wearable devices and health apps, also known as ‘mobile health’ (mHealth). mHealth companies do not only target individual consumers, but also businesses, as they see a market in corporate health and wellness programs. As such, some employers now offer employees to use fitness wristbands or smartwatches so that employees can monitor their health at work and beyond. These devices and apps enable users to track their exercise, number of steps, sleep patterns, eating habits, and a myriad of other health-related activities, which are typically not connected to work. For example, the Apple Watch Series 5 offers to monitor heart rate and glucose levels, and collects, the user's health information in the user’s iPhone Health app.’1 Employers present mHealth devices and apps as company ‘perks’ for employees. However, the use of mHealth devices may come at a price for employees, who may share their most personal information, namely their health information, with their employer and third parties, such as mHealth developers, and/or insurance companies. For example, an employee may share unwillingly or unknowingly information with his/her employer about his/her diabetes condition, heart disease, or insomnia. Sharing this information with the employer not only affects the employee’s right to privacy and data protection, but also his/her right to non-discrimination, as the employer could potentially take into account this health information in decisions ranging from promoting to dismissing the employee.
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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".