Evidence map›Paper›PMID 40489512›Full record

ArticlePloS one2025

Unraveling job demand-control-support patterns and job stressors as predictors: Cross-sectional latent profile and network analysis among Italian hospital workers.

Igor Portoghese, Maura Galletta, Georg F Bauer, Gabriele Finco, Ernesto d'Aloja, Marcello Campagna

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In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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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.

Igor PortogheseDepartment of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.ORCID 0000-0003-3700-8594
Maura GallettaDepartment of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
Georg F BauerDivision of Public and Organizational Health/Center of Salutogenesis; Department Public and Global Health at the Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich, Switzerland.
Gabriele FincoDepartment of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
Ernesto d'AlojaDepartment of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.
Marcello CampagnaDepartment of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Job Demand-Control-Support (JDCS) model postulates that patterns of job demand, job control, and social support lead to eight job types that are associated with well-being and health. This study employed latent profile analysis (LPA) to identify JDCS profiles among Italian hospital workers (n = 1464) and examined the predictive roles of role clarity and negative relationships at work on profile membership. Furthermore, adopting a network perspective, this study explored the interrelationships among JDCS factors within each identified profile. The LPA results revealed four profiles: isolated prisoner, moderate strain, low strain, and participatory leader. In addition, role clarity increased the likelihood of being included in the low-strain, moderate-strain, and participatory leader profiles. In contrast, negative relationships at work increased the risk of being included in the isolated prisoner profile. Finally, the results of network analysis revealed that networks differed across profiles in terms of density (interconnections between nodes) and edge strength (magnitude of relationships between nodes). Our study extends previous JDCS research by highlighting that researchers should consider empirically identified profiles rather than theoretically defined subgroups. The implications for stress theory, future research, and practice are also discussed.

Indexed as

Occupational StressPersonnel, HospitalSocial SupportAdultCross-Sectional StudiesFemaleHumansItalyJob SatisfactionMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40489512
PMCPMC12148104

What OpenQuestion holds

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LicenceCC BY
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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.