Evidence map›Paper›PMID 36608261›Full record

ArticleJMIR nursing2023

Nurses' Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions.

Haoqiang Jiang, Arturo Castellanos, Alfred Castillo, Paulo J Gomes, Juanjuan Li, Debra VanderMeer

Abstract read
In one paragraph

Article in JMIR nursing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Haoqiang Jiang *College of Informatics, Northern Kentucky University, Highland Heights, KY, United States.ORCID https://orcid.org/0000-0002-6788-9604
Arturo Castellanos *Mason School of Business, The College of William & Mary, Williamsburg, VA, United States.ORCID https://orcid.org/0000-0002-7477-7379
Alfred Castillo *Information Systems and Business Analytics Department, Florida International University, Miami, FL, United States.ORCID https://orcid.org/0000-0002-3498-2415
Paulo J Gomes *Information Systems and Business Analytics Department, Florida International University, Miami, FL, United States.ORCID https://orcid.org/0000-0001-7694-5500
Juanjuan Li *Nicole Wertheim College of Nursing & Health Sciences, Florida International University, Miami, FL, United States.ORCID https://orcid.org/0000-0002-3178-0236
Debra VanderMeer *Information Systems and Business Analytics Department, Florida International University, Miami, FL, United States.ORCID https://orcid.org/0000-0002-5930-6667

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWeb-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns.

objectiveThe objective of this study was to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic.

methodsWe analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses' evolving perspectives based on temporal changes.

resultsWe identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns.

conclusionsThe analysis demonstrates that professional web-based forums capture nuanced details about nurses' work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises.

Indexed as

blog dataCOVID-19machine learningnatural language processingpandemicstressorstext miningwork concerns

Identifiers

PMID36608261
PMCPMC9907981

What OpenQuestion holds

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LicenceCC BY
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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.