Work loss associated with increased menstrual loss in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of increased menstrual flow on the loss of work. METHODS: Heavy or otherwise abnormal menstrual bleeding is a common problem among women in the reproductive age range. Until now, there has been no evidence of its effect on absences from work. We used data from the National Health Interview Survey 1999, a personal interview household survey using a nationwide representative sample of the civilian noninstitutionalized population of the United States. Participants were 3133 women aged between 18 and 64 years who reported having a natural menstrual period in the last 12 months and in the last 3 months, never having taken medication containing estrogen (except past use of oral contraceptives), and never having been told that they had reproductive cancer. Analysis was performed using data from 2805 women, 373 having self-described heavy flow and 2432 having normal flow. The main outcome measure was work loss associated with the degree of menstrual flow. RESULTS: Using binary logistic regression, age, marital status, education, family size, perception of health, and flow of menstrual periods are associated with work losses (P <.05). The odds ratio of 0.72 (95% confidence interval 0.56, 0.92) indicates that women who have a heavier flow are 72% as likely to be working as are women who have a lighter or normal flow. CONCLUSION: Menstrual bleeding has significant economic implications for women in the workplace: work loss from increased blood flow is estimated to be $1692 annually per woman.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it