Proceso de evaluación para la incorporación de tecnologías en salud por instituciones gubernamentales en países que tienen un sistema universal de salud como Inglaterra, Brasil y Canadá, en una perspectiva comparada
Bibliographic record
Abstract
Introduction: Processes of Incorporation of Health Technologies are complex and involve multiple disciplines and stakeholders within an organization. Health systems have followed different models in their processes and sub processes for evaluation considering the systematic review, meta-analysis, models economic and/or fiscal impact, or involving stakeholders and/or the users and carers. Some countries have been more or less successful in incorporating technologies; considering the relationship between technological progress and health expenditures is important for the sustainability of the health system in the short and long term. Objective: Describe the evaluation process for the incorporation of health technologies by government institutions in countries with universal health care system like England, Brazil and Canada, in a comparative perspective Design: A descriptive and analytical study with a comparative approach where a systematic and thorough review of documents and articles published in three government institutions such as NICE, CADTH and CONITEC countries England, Canada and Brazil respectively performed. Results: The three countries finance their resource agencies and commissions of the state, which gives economic sustainability and well NICE has mandatory regulations that gives independence. Experience in structuring the ETS is very important as NICE and CADTH. The methodology is defined and transparent especially in NICE. Economic evaluations are performed in the three entities depends on the technology. Citizen participation in Brazil is still in the process of further. Most technologies are evaluated pharmaceuticals and 62% is new. Human resources are multidisciplinary and highly skilled. Only England has decision thresholds. The decision process rests with the Ministry of Health with the exception of England and the recommendations made by NICE are required. Conclusions: The process of incorporation of health technologies is given in the three countries and reflects specific contexts as the model of the health system and the complexity of the process and the institutions conducting the ETS and its financing.
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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 | 0.310 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".