Improving Self-Diagnosis Accuracy in Senegalese Diabetic Patients Using Mobile Applications: A Protocol
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2002
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| _version_ | 1866901861647253504 |
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| author | Duarte, Karinza Nhanhô Lima, Miroslauê Cabral Santos, Chica Alcântara |
| author_facet | Duarte, Karinza Nhanhô Lima, Miroslauê Cabral Santos, Chica Alcântara |
| contents | <p>This study addresses a current research gap in Medicine concerning ✅ Mobile Application for Diabetes Management Training among Senegalese Diabetic Patients: Self-Diagnosis Accuracy Improvement in Angola. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. ✅ Mobile Application for Diabetes Management Training among Senegalese Diabetic Patients: Self-Diagnosis Accuracy Improvement, Angola, Africa, Medicine, protocol This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18751977 |
| institution | Zenodo |
| language | eng |
| publishDate | 2002 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Improving Self-Diagnosis Accuracy in Senegalese Diabetic Patients Using Mobile Applications: A Protocol Duarte, Karinza Nhanhô Lima, Miroslauê Cabral Santos, Chica Alcântara African Geography Diabetes Management Mobile Applications Self-Diagnosis Validation Studies Community Engagement Data Analytics <p>This study addresses a current research gap in Medicine concerning ✅ Mobile Application for Diabetes Management Training among Senegalese Diabetic Patients: Self-Diagnosis Accuracy Improvement in Angola. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. ✅ Mobile Application for Diabetes Management Training among Senegalese Diabetic Patients: Self-Diagnosis Accuracy Improvement, Angola, Africa, Medicine, protocol This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p> |
| title | Improving Self-Diagnosis Accuracy in Senegalese Diabetic Patients Using Mobile Applications: A Protocol |
| topic | African Geography Diabetes Management Mobile Applications Self-Diagnosis Validation Studies Community Engagement Data Analytics |
| url | https://doi.org/10.5281/zenodo.18751977 |