Well‐being in healthy Icelandic women varies with extreme seasonality in ambient light
Bibliographic record
Abstract
Seasonal variation in photoperiod may affect psychosocial and physical well-being in healthy persons. We tested this hypothesis in healthy pre-menopausal women, without a history of mood disorders, living year-round in Reykjavik, Iceland (64.1°N). Participants reported daily self-assessments of well-being throughout a complete ovulatory menstrual cycle in summer and/or winter (70% participated in both seasons). Scores for mood, cognitive acuity, social support, physical health and a composite of these four indicators were each significantly higher in summer than in winter (linear mixed effects models: p < .001 for each model); tiredness did not differ by season. The effect of season was not significantly changed by inclusion of body mass index and/or age as covariates. Some prior studies have been hampered by sparse time sampling, inattention to covariates and/or relying on recalled data. This is to our knowledge the first investigation to test the study hypothesis with daily real-time data spanning complete ovulatory menstrual cycles in each of two seasons. This dense sampling has revealed modest seasonal variation in well-being in healthy women. Daylength (sunlight exposure) is likely a major, but not necessarily sole, factor in these seasonal differences in well-being; temperature is likely less important given Iceland's relatively moderate (for its high latitude) seasonal temperature swings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".