Evidence map›Paper›PMID 39049312›Full record

ArticleStudies in health technology and informatics2024

Advancing Data Science Competencies for Nursing PhD Students.

Suzanne Bakken, Caitlin Dreisbach, Sergey Kisselev, Meghan Reading Turchioe

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Studies in health technology and informatics, 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

4 authors.

Suzanne BakkenColumbia University, New York, New York, USA.
Caitlin DreisbachUniversity of Rochester, Rochester, New York, USA.
Sergey KisselevColumbia University, New York, New York, USA.
Meghan Reading TurchioeColumbia University, New York, New York, USA.

Funding

Data-driven shared decision-making to reduce symptom burden in atrial fibrillationR00NR019124 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TURCHIOE, MEGHAN READING · 2022 to 2024
$732k
NINR NIH HHS R00 NR019124
6 · The paper itself

Abstract

Given the evolving importance of data science approaches in nursing research, we developed a 3-credit, 15-week course that is integrated into the second year PhD curriculum at Columbia University School of Nursing. As a complement to didactic content, the students address a research question of their choice using a big data source, Jupyter Notebook, and R programming language. The course evolved over time with generative AI tools being added in 2023. Student self-evaluations of their data science competencies improved from baseline. This case study adds to the evolving body of literature on data science and AI competences in nursing.

Indexed as

CurriculumData ScienceEducation, Nursing, GraduateArtificial IntelligenceHumansNursing InformaticsStudents, Nursingdata scienceEducationgenerative AIvisualization

Identifiers

PMID39049312
PMCPMC12686978

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.