Vaccination against COVID-19 in Bogotá - Colombia: lessons and strategies in health pedagogy, risk communication and community participation, using behavioral sciences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Worldwide COVID-19 vaccination began in December 2020 as an event without precedents in public health history. Currently, around 12.000 million doses have been given, constituting a massive global effort to fight the pandemic. By June 2023, 70.3% of the world’s population has been vaccinated, which is almost the 80% of the goal set by the World Health Organization (WHO). Despite va¬ccine availability, literature describes misinformation amongst other complex and multifactorial challenges related to low vaccination coverages against COVID-19. Therefore, the success of vaccine initiatives globally highly depends on the strategies to strengthen pedagogy in public health and risk communication, so there is an adequate level of knowledge, acceptance and trust in the process and decision-making regarding COVID-19 vaccination. The objective of this revision is to present basic concepts, available tools, and recommendations for developing strategies on health pedagogy, risk communication, and community involvement to promote vaccination. In addition, these insights were based on lessons learned during the COVID-19 pandemic in Bogotá, where the effective implementation of a timely, clear, and targeted communication strategy based in behavioral science principles played a crucial role in the progress of vaccination efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it