ArticleHuman resources for health2016
Examining the influence of country-level and health system factors on nursing and physician personnel production.
Article in Human resources for health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Factors influencing clinical decision-making skills among nurses in Malaysia: A three-level general linear mixed-effects analysis.International journal of nursing studies advances · 2026Article
- Association between the rule of law and health-related sustainable development goals: an ecological study.BMC public health · 2025Article
- Economic and healthcare influences on circulatory diseases in Kazakhstan: a retrospective ecological study.Journal of health, population, and nutrition · 2024Article
- Article
- Physician density: will we ever close the gap?BMC research notes · 2023Article
- Influence of well-being and quality of work-life on quality of care among healthcare professionals in southwest, Nigeria.Scientific reports · 2023Article
- Pattern and perception of wellbeing, quality of work life and quality of care of health professionals in Southwest Nigeria.BMC health services research · 2022Article
- Health care workers and migrant health: Pre- and post-COVID-19 considerations for reviewing and expanding the research agenda.Journal of migration and health · 2021Article
- Work environment factors and provider performance in health houses: a case study of a developing country.BMC research notes · 2020Article
- Investing in human resources for health: beyond health outcomes.Human resources for health · 2016Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundA key component to achieving good patient outcomes is having the right type and number of healthcare professionals with the right resources. Lack of investment in infrastructure required for producing and retaining adequate numbers of health professionals is one reason, and contextual factors related to socioeconomic development may further explain the trend. Therefore, this study sought to explore the relationships between country-level contextual factors and healthcare human resource production (defined as worker-to-population ratio) across 184 countries.
methodsThis exploratory observational study is grounded in complexity theory as a guiding framework. Variables were selected through a process that attempted to choose macro-level indicators identified by the interdisciplinary literature as known or likely to affect the number of healthcare workers in a country. The combination of these variables attempts to account for the gender- and class-sensitive identities of physicians and nurses. The analysis consisted of 1 year of publicly available data, using the most recently available year for each country where multiple regressions assessed how context may influence health worker production. Missing data were imputed using the ICE technique in STATA and the analyses rerun in R as an additional validity and rigor check.
resultsThe models explained 63 % of the nurse/midwife-to-population ratio (pseudo R (2) = 0.627, p = 0.0000) and 73 % of the physician-to-population ratio (pseudo R (2) = 0.729, p = 0.0000). Average years of school in a country's population, emigration rates, beds-per-1000 population, and low-income country statuses were consistently statistically significant predictors of production, with percentage of public and private sector financing of healthcare showing mixed effects.
conclusionsOur study demonstrates that the strength of political, social, and economic institutions does impact human resources for health production and lays a foundation for studying how macro-level contextual factors influence physician and nurse workforce supply. In particular, the results suggest that public and private investments in the education sector would provide the greatest rate of return to countries. The study offers a foundation from which longitudinal analyses can be conducted and identifies additional data that may help enhance the robustness of the models.
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