ArticleFrontiers in psychology2026
Identifying profile-specific candidate targets for miner safety: a latent class and network analysis of psychological resources.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Understanding how psychological resources configure to sustain miner safety is essential for designing targeted interventions, yet remains limited. Methods: Data were collected from 1,247 Chinese frontline miners who completed measures of transformational leadership, job crafting, work engagement, person-job fit, organizational commitment, knowledge sharing, and safety performance. Latent class analysis (LPA) was applied to identify psychological profiles, and Gaussian graphical models with EBICglasso regularization were estimated for each profile. Network centrality, bridge centrality, and a network-based candidate target index were computed to prioritize profile-specific intervention targets. Network Comparison Tests (NCT) were used to formally compare network structures across profiles. Results: Four profiles emerged, namely Job-Crafting-Driven (42.8%), Leadership-Dependent (11.4%), Low-Resource Vulnerable (18.8%), and Optimal (27.0%). Network density was highest in the Job-Crafting-Driven class (0.560) and lowest in the Optimal class (0.320). Person-job fit and knowledge sharing consistently ranked among the top three bridge nodes across all four profiles. The candidate target index revealed profile-specific priorities, such as person-job fit and knowledge sharing for the Job-Crafting-Driven group, and organizational commitment for the Low-Resource Vulnerable group. NCT indicated significant global strength differences between Class 1 and all other classes, and significant network structure differences between Classes 1 and 4, and between Classes 2 and 3. Discussion: These findings provide a foundation for generating testable hypotheses about profile-specific safety interventions, pending longitudinal validation. Practically, safety interventions should be tailored to distinct worker profiles, with person-job fit and knowledge sharing as priority targets for most profiles. Optimal profile centrality estimates were statistically unreliable and excluded from interpretation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.