Longitudinal associations between bicycling and having dependent children, in middle-aged men and women
Bibliographic record
Abstract
Bicycling has multiple health benefits. Child-rearing may influence bicycling, but little is known about the impact of this between men's and women's bicycling, or of number and ages of children on bicycling. This study examined the longitudinal associations between having dependent children and bicycling for transportation and recreation over 4 years among mid-aged men and women. Data were from the HABITAT study (Australia). We analysed data from three survey waves (2007, 2009, 2011) using multilevel logistic regression stratified by gender (n = 7758). Findings indicate that having dependent children was associated with bicycling for transportation and recreation in contrasting ways for men and women. The odds of bicycling were higher in men with ≥2 children aged under 18y than men without children (transportation: OR = 1.93, 95% CI: 1.26, 2.98; recreation: OR = 2.37, 95% CI: 1.67, 3.37). Over time, the odds of recreational bicycling were lower in women with ≥2 children than women without children (OR = 0.83, 95% CI: 0.73, 0.93). However, for both men and women, the odds of recreational bicycling were higher in those with children aged 6-12y than those with younger or older children (men: OR = 1.86, 95% CI: 1.39, 2.49; women: OR = 1.79, 95% CI: 1.31, 2.46). Interventions to promote bicycling must therefore consider gendered differences in bicycling for travel and active leisure, and family circumstances. An opportunity to promote bicycling might be to target parents with children aged 6-12y.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".