Health Education and Vaccination for the Construction of Inclusive Societies
Bibliographic record
Abstract
Globalization has led to what has happened in a certain part of the world having a significant and rapid impact on other places, causing significant changes in health problems. In the last quarter of the 18th century, the history of vaccination began, becoming an effective tool to prevent and control communicable diseases. This paper proposes an observational research with a cross-sectional design to study the importance of health education and vaccination in building inclusive societies. With a sample of 1000 participants from 76 countries, vaccine awareness and regulation were analyzed, considering the following variables: gender, age, sector, Human Development Index (HDI), and continent. The instrument used was a questionnaire (VACUNASEDUCA) developed for this research and timely validated. As a result, it is highlighted that the profiles of women, people under 30 years of age, education sector, high Human Development Index, and European continent are those that most value the importance of raising awareness in society and the regulation of actions for vaccination compliance. The consequences of "vaccine reluctance" are of concern in every country on the planet. Therefore, it is concluded that effective and evidence-based communication is key to allaying fears and promoting acceptance of vaccination around the world, building inclusive societies in which all citizens enjoy the health benefits.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".