Determinants of Beneficiary Effects of Physical Activity among Adult Population in US
Bibliographic record
Abstract
Physical activity, if reaches to a beneficiary level, positively affect almost all the chronic diseases. The aim of this study was to determine the socio-demographic and anthropometric determinants of beneficiary effects of different domains of physical activity. Physical activity data from the National Health and Nutrition Examination Survey 2005-2006 were utilized. A cut off point of 7.5 MET-hour/week was used a level in which beneficiary effect of physical activity starts. Logistic regression model were used to evaluate the magnitude and the determinants of beneficiary effects for each domains of physical activity. The median of physical activity was mainly similar for leisure time, home and garden, and total physical activity across different categories of socio-demographic factors but not with Transportation domain. The transportation contributed up to 60% for age group 35-54 years, 35% for age group 55 to 64 years. Male enjoyed close to 37% more in achieving health benefit compared to female (the OR was 0.63 with 95% CI of 0.58, 0.69). Others factors played important role in different dominos of physical activity in achieving health benefits. Our findings indicated that achieving beneficiary effect of physical activity is highly depended on socio-demographic factors
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".