Title of Manuscript: Increase in national health insurance during the COVID-19 pandemic in Peru.
Bibliographic record
Abstract
Increasing health insurance has been a challenge during the pandemic in developing countries. For this reason, we analysed the regulations issued by the Peruvian government about health insurance and the health insurance affiliation at the national level during the pandemic. We carried out a cross-sectional study that included a review of national government regulations and an analysis of secondary data on people enrolled in health insurance. We identified eight national regulations oriented to foster health insurance from the last quarter of 2019 to the third quarter of 2021. We also found an increase in health insurance coverage at the national level, represented by insurer organisations: Comprehensive Health System (SIS) (72.5%), Social Security (ESSALUD) (27.6%), Private Insurers (2.7%), Armed Forces/Police’s insurer (1.9%), and other insurer companies (6.2%). The affiliation increased mainly in quintile 5 (23.4%) and quintile 4 (20.2%). During the pandemic, there have been developed and implemented regulations that have promoted health insurance at the national level; likewise, we found an increase in the number of enrolled people, with the greatest increase in the SIS.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".