Women's Perceptions of Influences on Their Mood
Bibliographic record
Abstract
AIMS: Knowledge of prevailing community ideas about mood determination can guide research about variability in mood. A random sample of urban Canadian women, aged 18-40 years (n = 507), was asked to compare the relative importance of three specified domains (physical health, social support, stress) as influences on their mood and then to list additional life experiences they considered important. They also rated the frequency and recurrence patterns (cyclicity) of their daily positive and negative moods. RESULTS: More women reported a positive overall mood than negative mood. Of three domains studied, social support was listed as the greatest influence on positive mood and stress on negative mood in the bivariate tests. More frequent moods (both positive and negative) were more likely to be viewed as recurrent or cyclical. Patterns of influence for positive mood differed from those for negative mood. Multivariate modeling found that women reporting frequent positive mood were more often North American and employed full-time and likely to consider stress or lack of stress was unimportant as an influence on positive mood. The only factors in the model associated with frequent negative mood were the perception of physical health and stress as important influences on negative mood. Less than 5% cited menstrual cycle phase as an influence. CONCLUSIONS: These subjective data suggest that women perceived a wide range of external, usually interpersonal, influences as relevant to their mood, however menstrual cycle was rarely mentioned. Perceptions of influences on mood are statistically related to frequency of moods. In addition, ethnicity and paid employment are independently associated with positive mood.
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 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 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".