Financial Determinants of Pharmaceutical Supply for SIS Insured in Pasco Region 2019-2021
Bibliographic record
Abstract
One of the most significant concerns in Peru’s public health system, given the lack of medicines provided in state health establishments, is that patients make use of their own resources to procure medicines. In this sense, the Integral Health Care Insurance (SIS by its acronym in Spanish for Seguro Integral de Salud) is especially relevant becuase it is the largest public insurance institution in Peru. The SIS’s objective is to providepriority coverage to Peru’s most vulnerable through monetary transfers to each regional healthcare Expenditure Entity (UE by its acronym in Spanish for Unidad Ejecutora) through signed agreements. Therefore, the author selected Pasco Region to use as a case study because it is one of the poorest regions of the country. This study was conducted with the objective of discovering the financial factors that determine the level of pharmaceutical supply in SIS insured people by use of the correlational method. Because the SIS is in charge of carrying out the budget coverage, their principle ability to positively affect pharmaceutical supplies is through financial means. However, the administration of the logistical processes for the acquisition of medicines for SIS insured people is managed by Expenditure Entities that received the financing –at the beginning of every year- And the UEs are expected to provide the health products and medicines demanded by the insured. The paper concludes with a discussion of results and recommendations to better the pharmaceutical supply of SIS Hospitals in Peru through financial means.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".