The need for more medical schools in medically underserved regions in Africa

Brimmo, Farida Oluwabukola; Babatunde, Akintunde; Ezefuna, Naomi Nmesoma; Kanu, Mary Sathela; Biziyaremye, Patrick

doi:10.1016/j.amsu.2022.104967

“…The shortage of physicians and healthcare workers in Africa can be associated to the lack of medical education and medical schools' capacity i.e. a condition that negatively contribute to the increase of healthcare system in the continent (Brimmo et al, 2022). The increase in the number of physicians for 1000 people is insufficient to reduce infant mortality in Bangladesh (Chowdhury et al,).…”

Section: Physicians and The Performance Of Healthcare Sectormentioning

confidence: 99%

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

Leogrande,

Costantiello,

Leogrande

2024

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In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to “Trust in the Police and Firefighters”, “Net Income Inequality”, and negatively associated, among others, to “Research and Development Intensity” and “Soil waterproofing by artificial cover”. Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

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“…[1], [2], [3], [4], [5], [6], [7], [8], [9], [10] Physicians and the performance of healthcare sector [11], [12], [13], [14], [15], [16], [17], [18] Physicians and economic growth [19], [20] 3) The Econometric Model for the Estimation of the Socio-Economic Determinants of Physicians Among Italian Regions…”

Section: Literature Review By Macro-themes Physicians and Covid-19mentioning

confidence: 99%

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

Leogrande,

Costantiello,

Leogrande

2023

Preprint

0

View full text Add to dashboard Cite

In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to “Trust in the Police and Firefighters”, “Net Income Inequality”, and negatively associated, among others, to “Research and Development Intensity” and “Soil waterproofing by artificial cover”. Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

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“…[1-10] Physicians and the performance of healthcare sector [11][12][13][14][15][16][17][18] Physicians and economic growth [19,20]…”

Section: Literature Review By Macro-themes Physicians and Covid-19mentioning

confidence: 99%

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

Leogrande,

Costantiello,

Leogrande

2023

Preprint

0

View full text Add to dashboard Cite

In the following article, we analyse the determinants of the number of physicians in the context of ISTAT BES-Benessere Equo Sostenibile data among twenty Italian regions in the period 2004-2022. We apply Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS-Ordinary Least Squares. We found that the number of Physicians among Italian regions is positively associated, among others, to “Trust in the Police and Firefighters”, “Net Income Inequality”, and negatively associated, among others, to “Research and Development Intensity” and “Soil waterproofing by artificial cover”. Furthermore, we apply the k-Means algorithm optimized with the Silhouette Coefficient and we find the presence of two clusters. Finally, we confront eight different machine-learning algorithms to predict the future value of physicians and we find that the PNN-Probabilistic Neural Network is the best predictive algorithm.

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The need for more medical schools in medically underserved regions in Africa

Cited by 5 publications

References 5 publications

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

The Socio-Economic Determinants of the Number of Physicians in Italian Regions

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