Evidence map›Paper›PMID 41988582›Full record

ArticleFrontiers in public health2026

Evaluating the effectiveness of the SHEL model in reducing occupational exposure risks among pre-hospital emergency personnel: a retrospective cohort study (2018-2024).

Yu Li, Xiaojuan Hou, Mingyi Qin

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. 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

3 authors.

Yu LiDepartment of Emergency, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xiaojuan HouDepartment of Endocrinology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Mingyi QinDepartment of Nursing, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pre-hospital emergency personnel face significant occupational exposure risks, yet traditional training approaches often prove inadequate. This study evaluated the effectiveness of SHEL (Software, Hardware, Environment, Liveware) model-based training versus standard operating procedure training (SOPT) in reducing occupational exposure incidents. Methods: This retrospective cohort study included pre-hospital emergency personnel from an emergency center between January 2018 and June 2024. Participants were divided into SOPT and SHEL groups based on the training received. Knowledge, Attitude, and Practice (KAP) scores and safety behavior compliance were assessed before and 6 months after training. Occupational exposure incidents and root causes were analyzed throughout the one-year follow-up. Psychological status was evaluated using the Nursing Stress Scale and Maslach Burnout Inventory before and 6 months post-training. Results: The analysis included 129 and 114 staff in the SOPT and SHEL groups, respectively. Six months post-training, the SHEL group demonstrated significantly higher KAP scores and safety behavior compliance (86.41 vs. 79.83, Conclusion: Training based on the SHEL model is more effective than traditional SOPT in enhancing occupational safety, reducing exposure incidents, and improving the psychological well-being of pre-hospital emergency personnel. These findings support the need for more integrated training approaches in emergency medical services.

Indexed as

Occupational ExposureAdultEmergency Medical ServicesFemaleHumansMaleRetrospective Studiesemergency medical servicesoccupational exposurepre-hospital emergency personnelSHEL modeltraining effectiveness

Identifiers

PMID41988582
PMCPMC13076328

What OpenQuestion holds

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Registered trials

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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.