The association between the initial outcomes of COVID-19 and the human development index: An ecological study
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Outcomes of the pandemic COVID-19 varied from one country to another. We aimed to describe the association between the global recovery and mortality rates of COVID-19 cases in different countries and the Human Development Index (HDI) as a socioeconomic indicator. METHODS: A correlational (ecological) study design is used. The analysis used data from 173 countries. Poisson regression models were applied to study the relationship between HDI and pandemic recovery and mortality rates, adjusting for country median age and country male to female sex ratio. RESULTS: During the first three months, the global pooled recovery rate was 32.4%(95%CI 32.3%–32.5%), and the pooled mortality rate was 6.95%(95%CI 6.94%–6.99%). Regression models revealed that HDI was positively associated with recovery β= 1.37, p = 0.016. HDI was also positively associated with the mortality outcome β= 1.79, p = 0.016. CONCLUSIONS: Our findings imply that the positive association between the HDI and recovery rates is reflective of the pandemics’ preparedness. The positive association between the HDI and mortality rates points to vulnerabilities in approaches to tackle health crises. It is critical to better understand the connection between nations’ socioeconomic factors and their readiness for future pandemics in order to strengthen public health policies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".