Evidence map›Paper›PMID 39136683›Full record

ArticleRevista da Escola de Enfermagem da U S P2024

Nursing competencies in aeromedical transport in the Brazilian Air Force: a descriptive study.

Leticia Lima Borges, Beatriz Gerbassi Costa Aguiar, Débora Fernanda Haberland, Clarissa Coelho Vieira Guimarães, Rafael Abrantes de Lima, Bianca Beatriz Silva de Souza

Abstract read
In one paragraph

Article in Revista da Escola de Enfermagem da U S P, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Leticia Lima BorgesUniversidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-2366-8092
Beatriz Gerbassi Costa AguiarUniversidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0001-6815-4354
Débora Fernanda HaberlandUniversidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0001-5448-6278
Clarissa Coelho Vieira GuimarãesUniversidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-7713-7182
Rafael Abrantes de LimaUniversidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0002-3843-7297
Bianca Beatriz Silva de SouzaUniversidade Federal do Estado do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.ORCID 0000-0001-5417-0222

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo analyze nursing skills in military aeromedical transport of the Brazilian Air Force.

methodDescriptive, qualitative research, carried out in three Brazilian Air Force hospitals in Rio de Janeiro, involving 64 military nurses. Discursive textual analysis identified competencies in healthcare, communication and decision-making.

resultsAfter characterizing participants, it was possible to understand the reality experienced by nursing professionals in air medical transport, highlighting the skills related to healthcare, communication and management, essential in all phases of air medical transport.

conclusionIt was evident that nursing assumes unique skills in caring for airborne patients, using diverse knowledge and experiences in solving problems encountered in the aeromedical work process. The need to implement continuing education strategies was also highlighted. The findings serve as support for professionals and managers to identify gaps in knowledge, performance and management of professional schedules in aeromedical transport.

Indexed as

Air AmbulancesClinical CompetenceAdultBrazilFemaleHumansMaleMilitary NursingMilitary Personnel

Identifiers

PMID39136683
PMCPMC11321455

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.