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Record W4391847461 · doi:10.55905/revconv.17n.2-108

The epidemiological impact of reduced childhood vaccination in Brazil: a desk research from 2012 to 2022

2024· article· en· W4391847461 on OpenAlexaff
Adriano Freitas de Santana, Eliane de Sousa Leite, José Ferreira Lima Júnior, Edineide Nunes da Silva, Kênnia Sibelly Marques de Abrantes, Kévia Katiúcia Santos Bezerra, José Normando Cartaxo Lopes

Bibliographic record

VenueContribuciones a las Ciencias Sociales · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsDeskEpidemiologyVaccinationEnvironmental healthMedicineFamily medicineVirologyPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Vaccination acts as the main method of preventing potential changes to global health, which have started outbreaks, endemics, epidemics and pandemics. Epidemiological profile’s analysis allows identifying, planning, executing and evaluating possible actions to control pathologies. A preventive control intervention that contributes positively to these epidemiological actions is immunization. This study aims to analyze the epidemiological impact of the reduction in childhood vaccination between 2012 to 2022. This is a documentary and epidemiological research of a retrospective nature that was carried out in April 2023, using free data, available at the Department of Informatics of the Unified Health System. A significant curve was observed relating vaccination between the years of the research, data demonstrates the continued importance of vaccination in preventing vaccine-preventable diseases. These numbers reflect the impact of the reduction in childhood vaccination and highlight the need for awareness raising and vaccination reinforcement actions to prevent the spread of vaccine-preventable diseases.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.448
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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