An assessment of socioeconomic status, access to healthcare, and education facilities in Indonesia following COVID-19: insights from the 8th High-Frequency COVID-19 World Bank survey
Why this work is in the frame
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Bibliographic record
Abstract
In 2023, Indonesians continued to grapple with the lingering impacts of the COVID-19 pandemic despite government measures introduced in 2020 to mitigate its effects. This study analyzed key factors affecting employment, food security, healthcare access, and education during the post-pandemic period in Indonesia, using data from the 8th Indonesian World Bank COVID-19 High-Frequency Survey (March–April 2023). The survey included 9751 adults (aged over 18 years) and 4,131 children (aged 5–18 years). Logistic regression was employed to identify determinants of employment, food security, healthcare access, and educational opportunities, with all statistical analyses conducted using IBM SPSS Statistics Version 29.0.2.0. Among adults, 49.5% were male, 28.2% lived on Java Island, and 61.4% resided in urban areas. Employment rates remained high at 84%, with 45.1% working as employees. Logistic regression analysis indicated that males had higher odds of being employed (AOR = 1.159, 95% CI: 1.158–1.160) but also faced slightly higher odds of experiencing food shortages (AOR = 1.043, 95% CI: 1.042–1.044). Employees, while more likely to encounter food shortages, also demonstrated greater odds of utilizing healthcare services, including regular check-ups (AOR = 1.094, 95% CI: 1.093–1.095), COVID-19 vaccinations (AOR = 1.759, 95% CI: 1.756–1.761), and telehealth services (AOR = 1.432, 95% CI: 1.428–1.437). Among children, 92.3% were enrolled in school, though 17.1% reported academic difficulties associated with sex, education level, region, location, online learning, study support at home, and parental employment sector (p < 0.05). Indonesia has made significant progress through programs such as the National Economic Recovery Program (PEN) and digital transformation initiatives, demonstrating adaptability and providing valuable models for similar contexts. However, improving the accuracy of social assistance databases and strengthening digital infrastructure for healthcare and education remain essential to ensure equitable outcomes and enhance resilience to future crises.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it