Evidence map›Paper›PMID 40614084›Full record

ArticleJMIR nursing2025

Using Large Language Models to Assess Burnout Among Health Care Workers in the Context of COVID-19 Vaccine Decisions and Health Beliefs: Retrospective Cohort Study.

Samaneh Omranian, Lu He, AkkeNeel Talsma, Arielle A J Scoglio, Susan McRoy, Janet W Rich-Edwards

Abstract read
In one paragraph

Article in JMIR nursing, 2025. 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.

Samaneh OmranianBrigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, MA, United States.ORCID 0000-0001-8974-2614
Lu HeCollege of Publich Health, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.ORCID 0000-0003-0181-018X
AkkeNeel TalsmaSchool of Nursing, University of Wisconsin-Milwaukee, Milwaukee, WI, United States.ORCID 0000-0001-6110-926X
Arielle A J ScoglioDepartment of Natural and Applied Sciences, Bentley University, Waltham, MA, United States.ORCID 0000-0002-3680-9405
Susan McRoyCollege of Engineering and Applied Science, University of Wisconsin-Milwaukee, 3200 Cramer Street, Milwaukee, WI, 53211, United States, 1 4142294000.ORCID 0000-0003-4826-0947
Janet W Rich-EdwardsBrigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, MA, United States.ORCID 0000-0001-5066-8808

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Burnout among health care workers affects their well-being and decision-making, influencing patient and public health outcomes. Health care workers' health beliefs and COVID-19 vaccine decisions may affect the risks of burnout. Therefore, understanding the interplay between these crucial factors is essential for identifying at-risk staff, providing targeted support, and addressing workplace challenges to prevent further escalation of burnout-related issues. Objective: This study examines how burnout is impacted by health beliefs and COVID-19 vaccine decisions among health care workers. Building on our previously developed Health Belief Model (HBM) classifier based on the HBM framework, which explains how individual perceptions of health risks and benefits influence behavior, we focused on key HBM constructs, including the perceived severity of COVID-19, perceived barriers to vaccination, and their relationship to burnout. We aim to leverage natural language processing techniques to automatically identify theoretically grounded burnout symptoms from comments authored by nurses in a large-scale, national survey and assess their associations with vaccine hesitancy and health beliefs. Methods: We analyzed 1944 open-ended comments written by 1501 vaccine-hesitant nurses, using data from the Nurses' Health Study surveys. We fine-tuned LLaMA 3, an open-source large language model with few-shot prompts and enhanced performance with structured annotation guidance and reasoning-aware inference. Comments were classified into burnout dimensions-Emotional Exhaustion, Depersonalization, and Inefficacy-based on the Maslach Burnout Inventory framework. Results: The model achieved a high weighted accuracy of 92% and an F1-score of 91% for Depersonalization. Emotional Exhaustion was identified in 52% (1003/1944) of comments, correlating strongly with perceived severity (189/323, 59%) and barriers to vaccination (281/650, 43%). Demographic analyses revealed significant variations in burnout prevalence, with older age groups reporting greater burnout. Conclusions: This study highlights the relationship between burnout and vaccine decision-making among health care workers, uncovering areas for further exploration. By exploring the complex interplay between psychological strain and vaccine hesitancy, this study sets the stage for developing transformative interventions and policies that could redefine workforce resilience and public health strategies.

Indexed as

Burnout, ProfessionalCOVID-19COVID-19 VaccinesHealth Belief ModelHealth PersonnelAdultDecision MakingFemaleHumansLarge Language ModelsMaleMiddle AgedRetrospective StudiesSurveys and QuestionnairesCOVID-19 VaccinesburnoutCOVID-19fine-tuninghealth beliefs modelhealth care workerslarge language modelsLLaMALLMsMaslach Burnout Inventorynursesprompt engineeringtext classification

Identifiers

PMID40614084
PMCPMC12248134

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.