Evidence map›Paper›PMID 39314645›Full record

ArticleSAGE open nursing

Descriptive Analysis of Student Diagnostic and Documentation Skill Development in a low Fidelity Simulation in Psychiatric Nurse Practitioner Education.

Nicholas Guenzel, Hongying Dai, Lyndsay Dean

Abstract read
In one paragraph

Article in SAGE open nursing. 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

3 authors.

Nicholas GuenzelCollege of Nursing, University of Nebraska Medical Center, Lincoln, NE, USA.ORCID https://orcid.org/0000-0002-6976-1970
Hongying DaiCollege of Public Health, University of Nebraska Medical Center, Omaha, NE, USA.
Lyndsay DeanCollege of Nursing, University of Nebraska Medical Center, Lincoln, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Nursing educators face challenges in helping registered nurses prepare to be psychiatric nurse practitioners. Nursing educators must consider the variation of students' skill sets, experience levels, and the shortage of preceptors. Simulations can help overcome limited clinical sites and standardize skill acquisition. High-fidelity simulations can be helpful but are labor-intensive and expensive. Low-fidelity simulations are adaptable to fit the learning needs of psychiatric nurse practitioner students but little research has evaluated these simulations. Objective: This paper evaluates a series of low-fidelity interventions used to help students learn diagnostic criteria, treatment decisions, and documentation. Methods: A retrospective descriptive analysis was performed on the results of all students in five cohorts in a psychiatric nurse practitioner course ( Results: The overall mean of description, detail, completeness, and professionalism increased from 7.6 (out of 10) to 9 over the semester with statistically significant improvements in all four areas. The strongest correlation was between detail and completeness and the weakest correlation was between professionalism and description. Students reported that they found significant value in the low-fidelity simulation exercises. Conclusion: This analysis demonstrates one of the first efforts to analyze low-fidelity simulations for psychiatric nurse practitioner students. The data indicate these simulations can help students improve their confidence and skills in several areas.

Indexed as

nursing educationpsychiatric nurse practitionersimulation

Identifiers

PMID39314645
PMCPMC11418308

What OpenQuestion holds

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Registered trials

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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.