Gender equality predicts leisure-time physical activity: Benefits for both sexes across 34 countries
Bibliographic record
Abstract
Although countries’ gender equality is associated with important health outcomes, especially for females, it remains unclear whether gender equality is associated with leisure-time physical activity (LTPA). Data from 34 countries was acquired from the International Social Survey Program, the Pew Research Forum, the United Nations, and the World Bank. Separate analyses were conducted for 21,502 males and 26,652 females. Hierarchal nonlinear Bernoulli modeling was used to examine the association between gender equality and participation in LTPA. Both males and females residing in countries’ with higher gender equality were more likely (twice and three times more likely, respectively) to report weekly LTPA than those residing in countries characterized by low gender equality. These effects persisted even when controlling for individual (i.e. age, education) and country-level (i.e. population, gross domestic product) covariates. However, significant variation in LTPA persisted at the country level, suggesting the need for further research. These findings provide novel evidence that both males and females benefit from gender equality. To explain these findings, we hypothesize that increased gender equality decreases the average number of offspring and, in turn, allows mothers more time for leisure, and to invest more resources in both male and female offspring, which may increase LTPA.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".