Understanding reasons for unmet health care needs in Korea: what are health policy implications?
Bibliographic record
Abstract
BACKGROUND: To ensure equal access to necessary care regardless of an individual's socioeconomic status, it is crucial to understand the factors that act as barriers. Unmet health care needs can arise for a variety of complex reasons, including personal choice, financial barriers, or lack of services, and each of these reasons requires a different policy approach. Researchers have advocated for a more granular measure of unmet health care need for better policy implication. This study aimed to assess various factors associated with different types of unmet health care needs in Korea. METHODS: The Korean National Health and Nutrition Examination Survey (KNHANES) 2010-2012 was used to analyze responses from 17,610 individuals over age 19. To measure the unmet needs of this population, self-reported experience in the past 1 year was used, and individual's reasons for unmet need were sorted into three distinct categories - availability, acceptability, accessibility. Four different logistic regression models stratified by gender were used to examine the relationship between socioeconomic factors and unmet needs. RESULTS: While income was not a significant factor for men, women with lower incomes showed a higher likelihood of experiencing unmet need. In addition, women with lower incomes showed higher odds of having acceptability-related unmet needs during the past 1 year compared to men. Education and income levels were associated with accessibility-related unmet needs for both women and men. CONCLUSION: As unmet health care needs are considered to be a critical indicator of a country's health care system, it is crucial to identify and eliminate any obstacles that prevent access to health care services. Under the current universal health care system in Korea, women, particularly those of lower income and lower educational levels, have limited access to necessary health care services. A gender-specific health care plan is recommended to reduce the higher rate of unmet needs experienced by this group. To reduce accessibility-related unmet needs, increasing available services for younger age groups, reflecting their needs of health services, needs to be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".