Procedimiento de medición de la demanda insatisfecha de la E.S.E. Camu Santa Teresita, Lorica-Córdoba
Bibliographic record
Abstract
When we refer to the issue of health, it is possible to affirm that Colombia has a great challenge in this matter. Among the many shortcomings that exist in the Colombian health system, one that can be highlighted has to do with the guarantee of the provision of health services. Today it is difficult to provide quality care and for it to be provided to the user in a timely manner. Above all, these difficulties arise in the timely care of medical specialties, authorization of highly complex tests and surgical procedures. This is reflected in the high rate of legal alternatives that users and patients have to resort to in order to access medical services. According to the Ombudsman, for the year 2020, between January and June, 46,113 guardianships were filed. The digital page, legal affairs, refers that the constitutional court reports for the first quarter of 2021 72,309 guardianships and 30,000 in other legal resources. Despite the difficulties mentioned above, our health system has two strengths; which are: the easy access and the wide coverage that the system has managed to establish during these last years. According to the accountability report of the Ministry of Health, for the year 2019, 95% of the population (47.9 million inhabitants) was insured and in the case of the migrant population for the years 2018 and 2019, the number increased. total affiliates in 788 thousand people. This is due, in part, to programs such as the "Pact for Colombia, Pact for Equity" Law 1955 of 2019, which are to guarantee access to users according to their different characteristics and particularities, where barriers to access and In this way, improvement actions are developed. Finally, the aim is to design a strategy that responds to the needs of the population, for which improvement techniques will be sought in the measurement of the demand of the existing dissatisfaction by the users of the ESE CAMU SANTA TERESITA and to be able to determine the needs of its users, in such a way to implement processes to improve their satisfaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".