The impacts of health insurance on financial strain for people with chronic diseases
Bibliographic record
Abstract
BACKGROUND: Due to ongoing expenses for both short-term and long-term needs for health services, people with chronic diseases tend to struggle with financial hardship. Health insurance is employed as a useful tool in aiding people to solve such financial strain. This study aims to examine and compare the impacts of public and private health insurance on solving financial barriers for people with chronic diseases. METHODS: This research obtained an outpatient sample consisted of 1739 individuals and an inpatient sample consisted of 1034 individuals. We employed a Chi-square test and a two-sample T-test to explore differences in financial strain and insurance status between people with chronic diseases and those without. Then we adopted binary logistic regression technique to assess the impacts of different types of health insurance on outpatient and inpatient financial strain for people with chronic diseases. RESULTS: Our research has five key findings: first, people with chronic diseases were more likely to experience both the outpatient and inpatient financial strain (P < 0.01); second, public health insurance was found to reduce the outpatient financial strain; third, private health insurance was found to positively associate with inpatient financial barriers; fourth, Urban Employment Insurance (UEI) was expected to reduce both the outpatient and inpatient financial barriers, while self-paid private insurance (SPI) was positively associated with inpatient financial barriers; and fifth, income was identified as a positive predictor of having outpatient and inpatient financial strain. CONCLUSIONS: Public health insurance has the potential to reduce the outpatient financial strain for people with chronic diseases. Private health insurance was identified as a positive predictor of inpatient financial strain for people with chronic diseases. Policy should be proposed to promote the capacity of public health insurance and explore the potential effects of private health insurance on solving the inpatient financial barriers faced by people with chronic diseases in China.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".