Children of Smoking and Non-Smoking Households’ Perceptions of Physical Activity, Cardiorespiratory Fitness, and Exercise
Bibliographic record
Abstract
Previous research has shown secondhand tobacco smoke to be detrimental to children’s health. This qualitative study aimed to explore children from low socioeconomic status (SES) families and their reasons for being physically active, attitudes towards physical activity (PA), fitness and exercise, perceived barriers and facilitators to PA, self-perceptions of fitness and physical ability, and how these differ for children from smoking and non-smoking households. A total of 38 children (9–11 years; 50% female; 42% smoking households) from the deprived areas of North West England participated in focus groups (n = 8), which were analysed by utilizing thematic analysis. The findings support hypothesised mediators of PA in children including self-efficacy, enjoyment, perceived benefit, and social support. Fewer than a quarter of all children were aware of the PA guidelines with varying explanations, while the majority of children perceived their own fitness to be high. Variances also emerged between important barriers (e.g., sedentary behaviour and environmental factors) and facilitators (e.g., psychological factors and PA opportunity) for children from smoking and non-smoking households. This unique study provided a voice to children from low SES and smoking households and these child perspectives could be used to create relevant and effective strategies for interventions to improve PA, fitness, and health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".