Menstrual cycle-associated symptoms and workplace productivity in US employees: a cross-sectional survey of users of the Flo mobile phone app
Bibliographic record
Abstract
Background: Mood and physical symptoms related to the menstrual cycle greatly affect women’s productivity at work and often lead to absenteeism. However, employer-led initiatives to tackle these issues are lacking. Digital health interventions focused on women’s health (such as the Flo mobile phone app) could help fill this gap given their anonymity, scalability and accessibility. Objective: We aimed to 1) measure the impact of disturbances related to the menstrual cycle on work-related productivity in users of the Flo app; 2) characterize levels of support and benefits women receive in the workplace and 3) explore whether the Flo app could help mitigate the impact of issues related to the menstrual cycle on productivity and absenteeism. Methods: 1867 users of the Flo app participated in a survey exploring 1) the extent to which their menstrual cycle negatively impacts their workplace productivity, including whether menstrual cycle-related symptoms led to absence from work in the previous 12 months; 2) whether Flo users feel supported by their manager and whether they receive specific benefits regarding issues related to their cycle and 3) the role of Flo in the management of menstrual cycle symptoms, preparedness, bodily awareness, openness with others, perceived support and mood.Results: The majority of Flo users reported a moderate to severe impact of their cycle on workplace productivity. 45.2% of the respondents reported absenteeism, with an average of 5.8 days of work missed due to their cycle. 48.4% reported not receiving any support from their manager and 94.6% said they were not provided with any specific benefit for issues related to their menstrual cycle. 75.6% declared they want such benefits. Users stated that the Flo app helped them with the management of menstrual cycle symptoms (68.7%), preparedness and bodily awareness (88.7%), openness with others (52.5%) and feeling supported (77.6%). Furthermore, users who reported the most positive impact of the Flo app were 18-25% less likely to report an impact of their menstrual cycle on their productivity and 12-18% less likely to take days off work for issues related to their cycle.Conclusions: The menstrual cycle can have a significant effect on workplace productivity and absenteeism, and resources available to employees are scarce. Digital health apps, such as the Flo app, could equip individuals with tools to better cope with issues related to their menstrual cycle and facilitate discussions around menstrual health in the workplace.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".