Preferences for public long-term care insurance among middle-aged and elderly residents: A discrete choice experiment in Hubei Province, China
Bibliographic record
Abstract
Objective It is critical to incorporate residents' preferences into the design of long-term care insurance (LTCI). However, little is known about middle-aged and elderly residents' preferences for personalized need-related attributes of LTCI in China. Through a discrete choice experiment (DCE), we aimed to focus on the direct beneficiaries of LTCI and then elicit their preferences for LTCI under a hypothetical scenario of dysfunction. Methods Attributes and levels were defined through a literature review and two rounds of expert consultations ( n = 8). A D-optimal fractional factorial design was used to generate the DCE questionnaire. Face-to-face interviews with middle-aged and elderly residents were conducted in two cities in Hubei Province, China, between November and December 2020. A mixed logit model was utilized for estimation. Results Five attributes were identified and incorporated into the DCE questionnaire. A total of 390 participants completed DCE questionnaires. Care facilities, care content, reimbursement rate, caregivers, and annual premium per person all had a significant impact on residents' preferences. Residents had significantly higher preferences for the LTCI scheme with home and community-based care centers (β = 1.40, p < 0.01), multi-level services (β = 0.44, p < 0.01), 90% reimbursement rate (β = 0.37, p < 0.01), and sufficiently trained caregivers (β = 0.26, p < 0.01). Individual characteristics, such as gender, employment, and education level were the factors that drove heterogeneity in preferences for LTCI. Conclusion This study provides new evidence on the preferences of middle-aged and elderly residents for personalized need-related public LTCI features. The design of the LTCI scheme in China needs to take these findings into account to maximize the utility for direct beneficiaries of LTCI and enhance their enrollment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".