Determinants of health insurance ownership in Jordan: a cross-sectional study of population and family health survey 2017–2018
Bibliographic record
Abstract
OBJECTIVES: With about one-third of the population living below the poverty line, Jordan faces major healthcare, social and national development issues. Low insurance coverage among the poor and high out-of-pocket expenditure worsens the financial insecurity especially for the marginalised population. The Government of Jordan aims to achieve universal coverage of health insurance-a bold plan that requires research evidence for successful implementation. In this study, we aimed to assess the proportion of the population covered by any health insurance, and the determinants owing a health insurance. DESIGN: A population-based prospective cohort study. SETTING: Jordan. METHODS: Data for this study were derived from the Jordan Population and Family Health Survey, which was implemented by the Department of Statistics from early October 2017 to January 2018. Sample characteristics were described as percentages with 95% CIs. Binary logistic regression models were used to estimate OR of health insurance ownership. Parsimonious model was employed to assess the sex and geographical differences. RESULTS: Data revealed that in 2017-2018, 73.13% of the 12 992 men and women had health insurance. There was no indication of age of sex difference in health insurance ownership; however, marital status and socioeconomic factors such as wealth and education as well as internet access and geographical location appeared to be the important predictors of non-use of health insurance. The associations differed by sex and urbanicity for certain variables. Addressing these inequities may help achieve universal coverage in health insurance ownership in the population. CONCLUSIONS: More than one-quarter of the population in Jordan were not insured. Efforts to decrease disparities in insurance coverage should focus on minimising socioeconomic and geographical disparities to promote equity in terms of healthcare services.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".