Examining the Relationship Between Occupational Physical Activity and Hypertension Status: Evidence from the Ghana WHO Study on Global Ageing and Adult Health
Bibliographic record
Abstract
The employment landscape has changed significantly over the past few years in emerging economies including Ghana where many people are rapidly transitioning from livelihood activities that originally involved physical exertion to work environments where performance of duties are mostly non-physical. Working under non-active conditions could, however, exacerbate the risk of developing chronic diseases that are increasingly becoming problematic in many developing countries. Drawing on a cross-sectional nationally representative sample data of 4425 adult Ghanaian workers collected by the WHO Study on Global Ageing and Adult Health in developing countries, we examined the relationship between occupational physical activity and hypertensive status. We employed the complementary log-log analysis technique to build nested models with results presented in odds ratios. After controlling for several relevant variables, the results show that performing sedentary related work (AOR = 1.23, CI = 1.06-1.42) is significantly associated with a higher likelihood of being hypertensive compared with those whose work involved moderate physical activity. Other factors that were significantly associated with being hypertensive included tobacco use (AOR = 1.33, CI = 1.05-1.70), living in an urban environment (AOR = 1.15, CI = 1.01-1.32), and being a female (AOR = 1.18, CI = 1.01-1.37) and being an older person. Policies on reducing the risk of developing chronic conditions especially hypertension need to recognize the contributions of the work environment in emerging developing economies.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".