Should I exercise or sleep to feel better? A daily analysis with active working mothers
Bibliographic record
Abstract
Positive affect is related to people’s health and appears to be a key determinant of happiness throughout the lifespan (Diener & Chan, 2011). Understanding the lifestyle factors that influence daily positive affect has been called for by experts and may identify small changes that people can make to enhance their mood. Physically active working mothers are an important population to study as they deal with time constraints and have lower mental health then their male counterparts. The purpose of this presentation is to report a daily diary study that was conducted with physically active working mothers to determine the relative influence of sleep satisfaction (the night before) and ratings of perceived exertion (RPE: the day of) on their positive affect at end of day. Sixty six women were recruited and had a mean age of 42.6 years, were employed full-time, had a least one child at home and engaged in recommended levels of PA. Variables were assessed using validated scales and were captured via electronic devices and data was analysed using HLM. When both RPE and sleep satisfaction were combined together in the model only RPE showed a significant relationship, t(750) = 3.923, p < .001. Results reveal robust associations between daily levels of physical activity and positive mood and implications for wellness promotion in working mothers are drawn.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".