Healthcare Professionals’ Perceptions of Non-Communicable Diseases Risk Factors and Its Regional Distribution in Ethiopia
Bibliographic record
Abstract
INTRODUCTION: Non-communicable diseases (NCDs) are increasing as the main cause of death, disability, unproductivity and indisposition in Ethiopia.OBJECTIVES: The objectives of this study were to establish healthcare professionals’ perception on non-communicable disease risk factors and their regional distribution in Ethiopia.METHODS: A mixed method sequential explanatory design was conducted with a questionnaire survey obtaining quantitative replies from 312 healthcare professionals working in 13 referral hospitals in the first phase and qualitative data among 13 hospital managers in the second phase.RESULTS: Statistically significant prevalence of NCDs risk factors were reported with the lack of physical exercise (M=4.94, SD=.245, t (311) = 139.383; p < .0005), hypertension (M=4.89, SD=.312, t (311) = 107.021; p < .0005), and unhealthy diet (M=4.61, SD=.782, t (311) = 36.426; p < .0005) ranking as the top three leading NCDs risk factors. The prevalence and distribution of NCDs risk factors varied within Ethiopia, with a high perceived prevalence of lack of physical exercise, unhealthy diet, alcohol use, and blood glucose in Addis Ababa city followed by Amhara region. A high prevalence of tobacco use and hypertension was also observed in the regions of Benishangul Gumuz.CONCLUSION: The results revealed that the prevalence of NCDs risk factors are increasing in different regions of Ethiopia. Regionally specific non-communicable disease intervention strategies are required to revert the growing burden of the risk factors effectively.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".