Voiceless at Work: Decision-Making Participation, Subjective Power, and Mental Health in a Pandemic
Bibliographic record
Abstract
Voice--the opportunity to express one's views in the decision-making process--is a central feature of organizational procedures. This study investigates the mental health consequences of the lack of voice at work, or voicelessness , during the first year of the COVID-19 pandemic, a period marked by heightened organizational change and uncertainty. Prior research on procedural justice and mental health has paid limited attention to the specific effect of voice, and few studies have used longitudinal designs that control for unobserved time-stable confounders. Moreover, the mechanisms that link procedural justice to mental health remain underexplored. We address these gaps by assessing the effect of voicelessness on psychological distress and anger using five waves of national longitudinal data of Canadian workers (March 2020 to April 2021) and fixed effects models. We further test whether the sense of mastery and subjective social status (SSS) mediate these relationships. Results show that voicelessness is associated with greater psychological distress and anger, net of time-stable confounders. Mastery functions as a mediator for both outcomes, whereas SSS does not. These findings underscore the importance of organizational efforts to enhance employee voice and identify perceived control as a key mechanism linking voicelessness to mental health.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".