Comparing the performance of the public, social security and private health subsystems in Argentina by core dimensions of primary health care
Bibliographic record
Abstract
BACKGROUND: Most Latin American health systems are comprised of public (PubS), social security (SSS) and private (PrS) subsystems. These subsystems coexist, causing health care fragmentation and population segmentation. OBJECTIVE: To estimate the extent of subsystem cross-coverage in a geographically bounded population (Rosario city) and to compare the subsystems' performance on primary health care (PHC) dimensions. METHODS: Through a cross-sectional, interviewer-administered survey to a representative sample (n = 822) of the Rosario population, we measured the percentage of cross-coverage (people with usual source of care in one subsystem but also covered by another subsystem) and the health services' performance by core PHC dimensions, as reported by each subsystem's usual users. We compared the subsystems' performance using chi-square analysis and one-way analysis of variance testing. We analyzed whether the observed differences were coherent with the predominant institutional and organizational features of each subsystem. RESULTS: Overall, 39.3% of the population was affiliated with the PubS, 44.8% with the SSS and 15.9% with the PrS. Cross-coverage was reported by 40.6% of respondents. The performance of the PubS was weak on accessibility but strong on person-and-community-oriented care, the opposite of the PrS. The SSS combined the strengths of the other two subsystems. CONCLUSION: Rosario's health system has a high percentage of cross-coverage, contributing to issues of fragmentation, segmentation, financial inequity and inefficiency. The overall performance of the SSS was better than that of the PrS and PubS, though each subsystem had a particular performance pattern with areas of strength and weakness that were consistent with their institutional and organizational profiles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".