Ebbs and flows: A within-person study of menstruation and work performance.
Bibliographic record
Abstract
Worker experiences are influenced by natural bodily fluctuations, yet these effects are rarely acknowledged by research, organizations, or society. For example, most women experience their menstrual cycle for most of their career, yet the relationship between women's menstrual cycle and work outcomes has received limited attention from organizational scholars and decision-makers. In this study, we explore how menstruation indirectly affects women's perceived daily work performance as mediated by emotional and self-control and how menstrual pain moderates these relationships. Drawing on theories of human energy and biological evidence related to the menstrual cycle, we conceptualize menstruation as an internal, chronic, and intermittent stressor that depletes potential energy, thereby limiting women's ability to engage in emotional and self-control, which in turn affect work behaviors. Given menstrual pain varies between individuals and throughout cycles, we conceptualize menstrual pain as a distinct internal stressor that can further deplete internal resources, moderating the relationship between menstruation and work behaviors. Results across 108 participants, over 30 consecutive days, indicate that compared with nonmenstruating days, when menstruating, women perceive a decreased capacity to engage in emotional and self-control. This in turn affected perceptions of their task performance, organizational citizenship behaviors, and work withdrawal. Menstrual pain amplified the relationship between menstruation and performance through emotional control, but not through self-control. Our findings emphasize how bodily fluctuations, specifically through the lens of menstruation, impact performance and underscore the need for employees, organizations, and society to move beyond ignoring these fluctuations to instead integrate them into workplace practices. (PsycInfo Database Record (c) 2025 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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".