Factors Associated with the Non-Use of Beneficiaries of Long-Term Care Insurance Service: The Case of Jeollanam-do Province
Bibliographic record
Abstract
Background: This study aimed to explore factors associated with the non-use of beneficiaries of long-term care insurance services for the elderly in Jeollanam-do Province by analyzing a dataset obtained from National Health Insurance Service. Methods: The study sample consists of 1,663 individuals who were evaluated as eligible for long-term care insurance services in Jeollanam-do Province during the period of July 1, 2008 through June 30, 2009. As a dependent variable, the non-use of the service was defined as one when a beneficiary had used it once or more times during one year after he or she was evaluated as eligible and as zero otherwise. A proportion analysis was conducted to describe characteristics of study sample. Chi-square tests were used to compare general characteristics between beneficiaries who had used the services and those who had not used them. Multiple logistic regressions were performed by three models including additional sets of explanatory variables such as socio-demographic characteristics, health conditions, and economic status. Results: Main results are summarized as follows. The proportion of beneficiaries who had not used the service was 14.5% of all beneficiaries. According to the results from the model using all explanatory variables, the factors associated with the non-use of the services were residence location, dwelling place, type of desired service, level of care needs, and instrumental activities of daily life limitations. Conclusion: In particular, regarding the type of desired service, the cash benefit showed a high likelihood of the non-use of the service; it had an odds ratio (OR) of 50.212 (95% confidence interval [CI], 24.00-105.04) compared with home service. In case of dwelling place, a hospital showed also a high likelihood of the non-use with an OR of 20.71 (95% CI, 10.12-42.44) compared with home.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".