Integration research, education and health services : background, strategies and initiatives
Bibliographic record
Abstract
This article aims to identify and analyze national and international experiences of integration in teaching, research and health services. From the experience in Municipal System of Health - School of Fortaleza, which integrates teaching, survey and assistance, interested us to know other experiences that take for granted such integration. In this sense, through a review of scientific literature, in bibliographic databases online, associated with a documentary research experiments that showed the same integration, we identified information indicating the existence of links between education, research and services. We selected eight experiments on the American continent, one in Canada, one in Cuba, two in Latin America and four in Brazil, which are presented in this article. Integrating teaching and research, using the face of the health system of care as an educational resource, it is clear as a unifying purpose in several countries of the Americas. Analyzing the 08 (eight) experiments presented, it is observed that in 06 (six) of them the integrate teaching, research and services already appears as a strategy for training and continuing education. However, the rapprochement between the functions of teaching, research and health services remains a battleground of convergence and divergence, therefore, as a space for conflict between different interests, making effective slowly. Thus, new investments must be made to uncover the dynamics and processes in construction to facilitate and foster the integration of teaching, research and health care, which requires inter practices, interagency and intersectoral.
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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 | 0.037 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".