Private Eyes, They See Your Every Move: Workplace Surveillance and Worker Well-Being
Bibliographic record
Abstract
Despite a surge in the number of organizations using surveillance technology to monitor their workers, understanding of the health impacts of these technologies in the broader working population is limited. The current study addresses this omission using a novel measure of an individual’s overall perception of workplace surveillance, which enables it to be asked of all workers, rather than only those in specific occupations or work contexts that have historically been vulnerable to electronic performance monitoring. Structural equation modeling analyses based on a national sample of Canadian workers ( N = 3,508) reveal that surveillance perceptions are indirectly associated with increased psychological distress and lower job satisfaction through stress proliferation. Findings demonstrate that the negative consequences of surveillance are explained by its positive association with three secondary work stressors: job pressures, reduced autonomy, and privacy violations. In the case of psychological distress, these stressors fully mediate a positive association with surveillance. The relationship between surveillance and job satisfaction is more complex, however, with the indirect effects of stress proliferation balanced out by a positive direct effect of surveillance on satisfaction. These results support the use of a stress process framework to examine how surveillance impacts worker well-being through stress proliferation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".