Designing an Educational and Training Program for Diabetes Health Educators at Diabetic Health Centers, Khartoum State, Sudan; 2007-2010
Bibliographic record
Abstract
BACKGROUND: By the year 2030 the number of diabetic patients is expected to reach 366 million worldwide (World Health Organization [WHO], 2013). METHODS: It was an intervention-facility based. The study focused on designing and implementing an educational and training program for health educators and to assess its effects on achievements of diabetes health educators; at Diabetic Health Centers in Khartoum State, Sudan; 2007-2010. The study population composed of diabetes health educators working in Diabetic Health Centers. Their total number was (36) and all were included in the study. Pre and post tests were done. Data was entered using SPSS. Parametric methods including t-test to assess result of training program before and after intervention were used. P-value ? 0.05 was considered statistically significant. RESULTS: Thirty six diabetes health educators attended the program. There was a significant statistical difference (p=0.001) between mean of Pre and post test concerning knowledge of diabetes health educators. To my knowledge there were no studies done, before, in Sudan to evaluate knowledge of diabetes health educators after attending an educational program in order to compare results with. CONCLUSION AND RECOMMENDATIONS: The designed education program improved health educators' knowledge. Educating diabetes health educators is important in program of diabetes management. In-serving training of diabetes health educators, aiding diabetic health centers with educational materials, and more research on evaluation of impact of education on diabetes health educators; was recommended.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".