[Innovation in the organization of health services delivery within the Metropolitan System of Solidarity in Peru].
Bibliographic record
Abstract
OBJECTIVE: Based on the results achieved to date by the Metropolitan System of Solidarity (SISOL) in Peru, this study undertook to analyze the extent to which SISOL has contributed to innovation in the organization of health services delivery. METHODS: SISOL performance indicators were analyzed and compared with those of other health services delivery models in Peru, drawing on data from a survey of 4 570 SISOL users conducted in the last quarter of 2011, National Household Surveys from 2003 through 2011, and statistical data from the Peruvian Ministry of Health and Social Security. RESULTS: SISOL rated high in terms of growth of the demand served in Lima, productivity of human resources in office visits, and levels of user satisfaction. These results are attributed to: (a) the presence of specialists at the first level of care; (b) an innovative public-private structure, as opposed to outsourcing; and (c) a system of incentives based on shared risk management. CONCLUSIONS: The findings support the need for primary health care renewal, especially in urban areas to reduce the proliferation of unnecessary levels and sublevels of care. They also point to the possibility of developing synergistic public-private partnerships in which both sectors share risks and act in collaboration within a single service system. And finally, they indicate that primary care needs to be articulated into the segmented models.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".