Mood and the Menstrual Cycle
Bibliographic record
Abstract
BACKGROUND: Premenstrual mood symptoms are considered common in women, but such prevailing attitudes are shaped by social expectations about gender, emotionality and hormonal influences. There are few prospective, community studies of women reporting mood data from all phases of the menstrual cycle (MC). We aimed (i) to analyze daily mood data over 6 months for MC phase cyclicity and (ii) to compare MC phase influences on a woman's daily mood with that attributable to key alternate explanatory variables (physical health, perceived stress and social support). METHOD: A random sample of Canadian women aged 18-40 years collected mood and health data daily over 6 months, using telemetry, producing 395 complete MCs for analysis. RESULTS: Only half the individual mood items showed any MC phase association; these links were either with the menses phase alone or the menses plus the premenstrual phase. With one exception, the association was not solely premenstrual. The menses-follicular-luteal MC division gave similar results. Less than 0.5% of the women's individual periodogram records for each mood item showed MC entrainment. Physical health, perceived stress and social support were much stronger predictors of mood (p < 0.0001 in each case) than MC phase. CONCLUSIONS: The results of this study do not support the widespread idea of specific premenstrual dysphoria in women. Daily physical health status, perceived stress and social support explain daily mood better than MC phase.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".