The relationship between democracy and corruption and the global physician workforce
Bibliographic record
Abstract
BACKGROUND: Understanding how governance factors such as democracy and corruption impact the healthcare workforce is crucial for achieving Universal Health Coverage (UHC). Effective health workforce planning and resource allocation are influenced by these political constructs. This study examines the relationship between democracy and corruption and key healthcare workforce metrics. METHODS: A cross-sectional study was conducted using a global dataset from 2020 to 2022. The primary outcome was Physician Density (medical doctors per 10000 people). Secondary outcomes included the generalist to specialist ratio and the percentage of female physicians (% Female). Partial correlations, multivariate analysis of variance (MANOVA), and univariate analysis of variance (ANOVA) were used to analyze the relationship between workforce variables and the democracy index (DI), and corruption perception index (CPI), controlling for domestic health expenditure. RESULTS: Data from 134 countries showed significant positive associations between both DI (r = 0.32, p = 0.004) and CPI (r = 0.43, p < 0.001) with physician density. MANOVA indicated significant multivariate effects of DI (Wilks' Lambda = 0.8642, p = 0.013) and CPI (Wilks' Lambda = 0.8036, p = 0.001) on the combined workforce variables. Univariate ANOVAs showed that DI (F = 6.13, p = 0.015) and CPI (F = 10.57, p = 0.002) significantly affected physician density, even after adjusting for domestic expenditure (F = 18.53, p < 0.001). However, neither DI nor CPI significantly impacted the Generalist to Specialist Ratio or % Female Physicians. DISCUSSION: Higher levels of democracy and lower levels of corruption are associated with a greater density of medical doctors, independent of healthcare spending. Policymakers must advocate for governance reforms that support a robust healthcare workforce to support aim of universal health coverage.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".