THE IMPACT OF SES ON THE ASSOCIATION BETWEEN PHYSICAL ACTIVITY AND HRQOL OVER A 3-YEAR PERIOD
Bibliographic record
Abstract
More research is needed on whether socioeconomic status influences physical activity and health related quality of life (HRQOL). Such information may inform health policies on improving healthy ageing in all strata of the population. The aim of this study is to assess, in an older adult community living sample consulting in primary care, the effect of socioeconomic status, based on a validated index score, on the effect of physical activity and health related quality of life. The study population included a sample of 1,801 community living older adults recruited in primary care clinics, of which 1,040 were also interviewed at follow-up, 3 years later. Health related quality of life was measured with the EQ-5D-3L. Physical activity was assessed with the following question: “How many times a week do you exercise for more than 20 minutes (for example, walking at a rapid pace)”. Responses were then categorized into 4 categories as follows: 0 times (never); 1 to 3 times; 4 to 7 times; 8 times and more a week. Generalized linear models (GLM) with repeated measures was used to study the change in HRQOL as a function of physical activity, stratified by socioeconomic status, controlling for potential important confounders such as gender, smoking status, alcohol consumption at least twice a week every week in past 6 months (yes/no), self-perceived physical and mental health status and number of chronic disorders. The results showed that HRQOL decreases with time and this decrease may be mitigated with physical activity in those with lower socioeconomic status. Promoting physical activity may limit social inequalities in health among low socioeconomic status populations.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| 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".