Are Israelis becoming healthier? Trends in self-rated health, 2002–2018
Bibliographic record
Abstract
BACKGROUND: Life expectancy at birth in Israel is steadily increasing. This raises the question whether Israelis are becoming healthier. The purpose of this study is to estimate trends in morbidity and to try to explain what causes morbidity levels to change. METHODS: We used 17 years of repeated cross-sectional data from the Social Survey to estimate trends in self-rated health. We used regression models to explain the trends in self-rated health that were observed from 2002 to 2018. RESULTS: Four major findings emerged. First, morbidity as measured by self-rated health has declined. Second, gains in educational attainment do not explain the decline in morbidity. Third, the rise in national expenditure on health per capita is strongly correlated with the decline in morbidity. And fourth, the effect of the national expenditure on health per capita appears to be stronger among women and among those without an academic degree. CONCLUSIONS: Self-rated health has improved. However, it has not improved to the same extent for all Israelis. The results of this study show that the health of women has improved more than that of men and that the health of non-academics has improved more than that of academics. The latter suggests that the progressive effect of public financing has offset the regressive effect of out-of-pocket payments on self-rated health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".