Health Equity as a Challenging Goal for Policymakers: A Systematic Review
Bibliographic record
Abstract
The ultimate goal of every health system is to maintain and promote community health by ensuring the equitable utilization of health services. In recent years, many countries have undergone health sector reform to improve health equity, but most continue to face challenges ahead. This study reviewed the effects of health system reform on reducing health inequity with a focus on the social determinants of health since 2000. This systematic review included an evidence search within the PubMed/Medline, Google Scholar, and Scopus medical research databases. Out of 1,559 published articles between 2000 and 2014, 29 who met the inclusion and exclusion criteria were chosen. Most countries studied considered financial intervention, such as increasing governmental health expenditure and insurance coverage, to establish universal health coverage. Primary care has been neglected in many reform plans as most countries focused on inpatient or outpatient care. None of the reforms have been entirely successful and health inequity remains among different socioeconomic groups. The articles highlighted the significance of socioeconomic and political determinants on the success rate of reforms within the study context. Moreover, strengthening primary health care, implementing stepwise reform, establishing a robust monitoring system and considering quality along with quantitative coverage is necessary to reduce health inequity.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".