Factors Associated With Preferences for Health System Goals in Japan
Bibliographic record
Abstract
Preferences among people for health system goals are important determinants in developing health policy. The aim of this study was to determine preferences for health system goals and their associations with sociodemographic characteristics in Japan. Participants were randomly selected from the general population in 5 prefectures and were asked to rank 5 health system goals in order of preference: health, health inequality, responsiveness, responsiveness inequality, and fair financing. Associations between sociodemographic characteristics and preferences for health system goals were examined using multinomial logistic regression analysis. A total of 4936 persons responded to this study. Health system goals in order of preference were health inequality (37.6%), responsiveness inequality (20.9%), health (18.4%), responsiveness (16.0%), and fair financing (7.1%). Sociodemographic characteristics such as gender, age, family status, education completed, and usage of health care services were associated with the preferred health system goal. Health policy makers should take these associations into account when developing prospective policy.
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 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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".