Vinculación de datos administrativos y su utilidad en salud pública: el caso de Ecuador
Bibliographic record
Abstract
The objective of this article is to describe the characteristics of addressing the linkage of administrative databases and the uses of such linkages in public health research, and also to discuss the opportunities and challenges for implementation in Ecuador. The linkage of databases makes it possible to integrate a person's data that may be scattered across different subsectors such as health, education, justice, immigration, and social programs. It also facilitates research that can inform more efficient management of social and health programs and policies. The main advantages of using linked databases are: diversity of data, population coverage, stability over time, and lower cost in comparison to primary data collection. Despite the availability of tools to process, link, and analyze large data sets, there has been minimal use of this approach in Latin American countries. Ecuador is well positioned to implement this approach, due to compulsory use of a unique ID in health services delivery, which permits linkages with other national information systems. However, the country faces several cultural, technical, ethical, legal, and political challenges. To take advantage of its potential, Ecuador needs to develop a data governance strategy that includes standards for data access and data use, as well as mechanisms for data control and quality, greater investment in professional training in data use both within and beyond the health sector, and collaborations between government entities, universities, and civil society organizations.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".