Gestión financiera y su repercusión en las prestaciones asistenciales en el Hospital Especializado III – Víctor Lazarte Echegaray – 2020
Bibliographic record
Abstract
The objective of this research is to Determine the Impact of Financial Management on the Assistance Benefits of the Specialized Hospital III - Víctor Lazarte Echegaray of Trujillo - 2020.; based on determining the procedures and methods of the billing system, as well as optimizing the management of non-tax income, in order to guarantee the economic sustainability of the Specialized Hospital III - Víctor Lazarte Echegaray in the city of Trujillo. The results show that the fulfillment of the programmed goals during the first quarters of 2020 is not as expected, in the second quarter the goal is achieved, but it is still low according to the Institutional Operating Budget evaluation criteria. The research presented is applied with a non-experimental, cross- sectional descriptive design. The technique used is documentary analysis, the population is made up of the documentation of the 10 Assistance Centers of the La Libertad Assistance Network. The sample is the Specialized Hospital level III - Víctor Lazarte Echegaray of the city of Trujillo. In conclusion, it was determined that the Financial Management in the Specialized Hospital III Víctor Lazarte Echegaray of the La Libertad Assistance Network, is deficient, because the proposed goals, guidelines and compliance with the provisions issued by the superior areas are not met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".