Barriers to Integrate Physical Exercise Into the Ethiopian Healthcare System to Treat Non-Communicable Diseases
Bibliographic record
Abstract
Introduction: Physical exercise is recognized as one component of non-communicable disease prevention, but little attention has been devoted to integrating physical exercise into the Ethiopian healthcare system, with the barriers to its inclusion being unclear. Objectives: The present study explores the bottlenecks to integrate physical exercise into the Ethiopian healthcare system to treat non-communicable disease. Design: A mixed method sequential explanatory design. Setting: Public referral hospitals in Ethiopia. Methodology: Data was collected in two phases among 312 (195 males and 117 females) healthcare professionals. The participants were selected proportionately and randomly from 13 public referral hospitals. Results: Lack of: national coordination to promote physical exercise (t (311) = 69.20, p < .0005), trained physical exercise professionals (t (311) = 14.42, p < .0005); physical exercise guidelines (t (311) = 33.25, p < .0005); training how to prescribe physical exercise by healthcare providers (t (311) = 62.94, p < .0005); information on the health benefits of physical exercise to give to their patients (t (311) = 65.62, p < .0005); and built environment that encourages physical exercise participation (t (311) = 59.64, p < .0005) were identified as barriers. Additionally, built environment, policy, healthcare professionals' lifestyle, demography of healthcare professionals, health information coverage of physical exercise and the hospital physical building were also identified as barriers. Conclusions: Physical exercise appears marginalized from the Ethiopian healthcare system. Healthcare organizations and policy makers could take the cited barriers into consideration to plan, design and integrate physical exercise into the healthcare system to prevent NCDs in Ethiopia.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".