Prevalence of Poor Diabetes Self-Management Behaviors among Ethiopian Diabetes Mellitus Patients: A Systematic Review and Meta- Analysis
Bibliographic record
Abstract
BACKGROUND: Diabetes has no cure so far, but appropriate self-management contributes to delay or control its progression. However, poor self-management by diabetic patients adds to disease burden. The pooled prevalence of overall, and its main components of poor self-management among Ethiopian diabetic patients remain elusive. Hence, this study aimed to determine the prevalence of poor diabetes self-management behaviors among diabetic patients in Ethiopia. METHOD: by using different combinations of search terms, we accessed articles done until February 15, 2020 through Pubmed, Scopus, Web of Science and Embase databases. Newcastle-Ottawa quality assessment scale was used for quality assessment, and STATA version 14 software along with the random-effects model was employed for statistical analyses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA.) guideline was followed to report the results. RESULT: Twenty-one studies with 7,168 participants were included in this meta-analysis. The overall pooled prevalence of poor self-management behavior among diabetic patients in Ethiopia was 49.79% (95% CI: 43.58%, 56.01%). Based on subgroup analysis, the estimated magnitudes of poor self-management by regions were 68.58% in Tigray, 55.46% in Harari, 54.74%, in Amhara, 40.90%, in SNNPRS and 37.06% in Addis Ababa. The worst (80.91%) and relatively better (24.65%) self-management components were observed on self-blood glucose monitoring and medication adherence, respectively. CONCLUSION: One in two diabetic patients in Ethiopia had poor self-management. Thus, we strongly recommend to the ministry of health and universities to train diabetes health educators, and the health facilities to deliver tailored diabetes health education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".