Nursing practices in vaccination: An integrative review
Bibliographic record
Abstract
In this paper, an integrative literature review was carried out aiming to answer: what is the nursing scientific production about vaccination practice? Research conducted in the Medline, SciELO, Lilacs, BDENF, PubMed, CINAHL and Scopus databases from 2010 to 2017. A sample of 49 papers was obtained. Most of the papers were classified with level VI of evidence. The highest level found was the II, controlled and randomized studies of level III accounted for 10.20%. The papers were grouped by similarity into four thematic categories: vaccine coverage 42.86%, administration of vaccine 28.58%, vaccination education 14.28% and management/supervision 14.28%. It management/supervision and education about vaccination are highlighted in most papers. The methodology used allowed the analysis and summarization of papers with different approaches. It is evident that vaccination goes far beyond the simple fact of administering an immunobiological, requiring an extensive and complex body of knowledge, with frequent updating of health professionals, especially nurses. Vaccination actions are effective in preventing diseases, and it is important to prioritize such actions in their daily practice. In nursing vaccination practices, nurses should assert their supervisor assignment, contributing to the organization of the service, continuing education of nursing staff, planning strategies to reach the goals of vaccination, evaluating vaccination coverage, working according to the population, through their education and awareness, and contribute with the body of knowledge about vaccination.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".