Improving diabetes mellitus care in Nigeria – health promotion and education perspectives
Bibliographic record
Abstract
Introduction and aim. In this review, we suggest ways to improve diabetes mellitus (DM) care in Nigeria from a Health Promotion and Education (HPE) perspective by addressing the gap in DM care through the adoption of strategies from the Ottawa Charter and National Health Promotion Policy (NHPP) guidelines. Material and methods. This review conducted a comprehensive literature search on Africa Journal Online, PubMed, Google Scholar, and Science Direct, from 1986 to 2023, using relevant keywords. Analysis of the literature. The adoption of the Ottawa charter and NHPP remains a key strategy in addressing the gap in DM care in Nigeria. This could be achieved by the adoption of population-focused multi-sectoral interventions encompassing legislation, regulation, and fiscal measures, creating sustaining and expanding health-promoting environments to reduce modifiable risk factors, and reorienting the primary health care services to aid the diagnosis, treatment and rehabilitation of DM patients. Conclusion. This review concluded that the government and other critical stakeholders should adopt the HPE strategies that covers increased financing, strict legislation on DM modifiable risk factors, reorientation of the primary healthcare system, and capacity building for HPE practitioners into DM care in Nigeria as a strategy to improving DM care and prevention in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".