Too depressed and anxious to speak up: The relationships between weekly fluctuations in mental health and silence at work.
Bibliographic record
Abstract
While it is widely acknowledged that some employees are more prone to silence than others, emerging research suggests that silence is much more dynamic than previously indicated, as even the most vocal employee will withhold input in some situations. However, given scant empirical attention to intraindividual fluctuations in silence, several important questions remain regarding its etiological antecedents, the mechanisms underlying such effects, and potential factors mitigating them. We respond by integrating the silence and mental health literature to consider how fluctuations in employees' experiences of depression and anxiety relate to fluctuations in silence via distinct silence motives. Specifically, we propose that employees are likely to engage in silence while experiencing episodes of depression because depressive symptomology shifts perceptions toward voice being pointless (i.e., ineffectual silence motive). Likewise, we propose that employees are likely to engage in silence while experiencing flare-ups of anxiety because anxious symptomology shifts perceptions toward voice being dangerous (i.e., defensive silence motive). Finally, we argue that voice endorsement attenuates these relationships by interrupting the link between silence motives and behaviors, such that employees experiencing heightened ineffectual and defensive silence motives are less likely to remain silent during weeks in which they experience high voice endorsement. We find support for these predictions via an experience sampling methodology study conducted with 136 employees across 4 weeks. We discuss how these results enhance theoretical clarity on the dynamic links between mental health and silence and offer insights into how organizations can counteract intrapersonal variations in silence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".