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Record W4402435259 · doi:10.15584/ejcem.2024.3.24

Improving diabetes mellitus care in Nigeria – health promotion and education perspectives

2024· article· en· W4402435259 on OpenAlexaboutno aff
Otovwe Agofure, Oluwafunmilayo Oluwaseun Abiodun, Oyediran Emmanuel Oyewole

Bibliographic record

VenueEuropean Journal of Clinical and Experimental Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusPromotion (chess)MedicineHealth promotionFamily medicineNursingGerontologyEnvironmental healthPolitical sciencePublic healthEndocrinology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.390
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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