EARLY AWARENESS AND ALERT ACTIVITIES IN LATIN AMERICA: CURRENT SITUATION IN FOUR COUNTRIES
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to briefly describe the current state of early awareness and alert (EAA) activities and systems in four Latin-American countries (Argentina, Brazil, Colombia, and Mexico). METHODS: Key informants were selected and completed an open questionnaire that included the following domains: current state of EAA activities and systems in each country, potential role for EAA systems in the health system, and future EAA projects that are currently being considered. RESULTS: In all four countries, health technology assessment (HTA) processes are used to prioritize the use of health resources, albeit at varying degrees and with different mechanisms and methodologies. EAA activities are still limited and there are virtually no institutions or units with specific functions explicitly devoted to EAA activity. However, most countries have developed some initial forms of EAA systems. Being in its initial stages there is no clear differentiation between these early awareness activities and other HTA functions, and no specific methodologies or processes are used to anticipate the emergence of new technologies. Consequently, early evaluation of technologies generally occurs in a reactive manner, after they have been introduced in the market and under the pressure of different stakeholders. CONCLUSIONS: There is growing awareness that the early identification and assessment of emerging technologies should be an integral part of HTA and the decision-making process. Many initiatives are currently focusing on building partnerships between the various regulatory bodies involved in the incorporation of technologies at national levels. It is reasonable to foresee that EAA activities will continue to develop and expand in the region.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".