Evaluación de la necesidad de implementación de un modelo de gestión con enfoque basado en procesos para mejorar la calidad de atención y los servicios de salud en el Centro de Especialidades Médicas “Vacari” durante el primer trimestre del 2017
Bibliographic record
Abstract
The present study aimed to evaluate the need to implement a management model with a process-based approach, according to the perception of satisfaction of users of Vacari Medical Center in the first quarter of 2017, in order to restructure internal processes, to Improve the quality of care and achieve a competitive advantage. The Methodology of the study was cross-sectional observational, with a sample of 30 randomly selected users of the Vacari Medical Center, who were given a structured survey, elaborated particularly for this study, based on others conducted in the country. As a result of the evaluation it was obtained that 50 percent of respondents considered little, 27 percent acceptable and 23 percent a lot of the time they waited to be taken care of by the health professional. In relation to the internal processes as: treatment of the doctor and of the nursing staff, clarity of the professional and its recommendations, the privacy and the time of attention; The users have a good perception of the services, however, there is a minimum percentage that does not consider it that way. 33 percent of users feel very satisfied, 63 percent satisfied, and only 3 percent dissatisfied. Of the infrastructure, 10 percent consider it regular and 7 percent bad, which, added to the cleaning of facilities, 17 percent consider regular and 3 percent as bad. In general terms, 60 percent of the respondents consider the care provided by the Medical Center to be excellent, and 83 percent consider the costs to be economic; 100 percent of respondents indicated that they would recommend it for the care they received if they would return to the Medical Center. In general, surveys reveal high levels of user acceptance; However, the low perception regarding infrastructure and cleanliness can not be overlooked. Therefore, the evaluation carried out in the study allows us to conclude that, in order to improve and maintain the quality of services, it is necessary to implement A Management Model with a process-based approach, as a fundamental step in improving quality.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".