Evidence map›Paper›PMID 37360268›Full record

ArticleTeaching and learning in nursing : official journal of the National Organization for Assciate Degree Nursing2023

Predictors of success in the registered nurse licensure exam for nursing graduates whose education was affected by the COVID-19 pandemic.

Hui-Ling Lai, Shu-Ying Fu, Tai-Chu Peng

Open access · greenAbstract read
In one paragraph

Article in Teaching and learning in nursing : official journal of the National Organization for Assciate Degree Nursing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.6field-weighted citation impact, top 17% of its field
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

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. Article
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

3 authors at 1 institution in 1 country.

Hui-Ling LaiDepartment of Nursing, Tzu Chi University, Hualien, Taiwan.
Shu-Ying FuDepartment of Nursing, Tzu Chi University, Hualien, Taiwan.
Tai-Chu PengDepartment of Nursing, Tzu Chi University, Hualien, Taiwan.
Tzu Chi University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on health-care education. However, the relationship between changes in nursing internships in Taiwan during the COVID-19 pandemic and outcome in the national registered nurse (RN) licensure exam for new nursing graduates is underexplored. The study was to explore the predictors of first-attempt success in the RN licensure exam in 2022. A retrospective review of secondary data was employed in this study. Adjusted binary logistic regression was used to analyze data. A convenience sample of 78 new graduates attempted the exam. Of these graduates, 87.2% passed the RN licensure exam. Age was the main predictor of exam success, followed by grand mean academic score and total alternative (nontraditional in-person) internship hours. Compared with those who failed the exam, the graduates who passed the exam were significantly more likely to be younger, have better academic performance, and have engaged in more alternative internship hours. Nursing faculties should consider implementing supportive strategies early for students who are underperforming or those who are older than their classmates on average to help them pass the RN exam on the first attempt. The optimal duration and long-term consequences of alternative nursing internships must be analyzed in further detail.

Indexed as

Distance learninginternshiplicensurenursing education

Identifiers

PMID37360268
PMCPMC10080274
OpenAlexW4362697952

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.