Evidence map›Paper›PMID 41625737›Full record

ArticleFrontiers in medicine2025

Determinants of Chinese physicians' engagement in narrative medicine: a comprehensive SEM-ANN analysis.

Jiaqi Li, Zhuomu Hu, Xinyu Gu, Yan Wu, Wei Sun, Yufei Yang, Yexin Hu, Yuxiao Li, Hui Zhang, Hao Zhang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2025. 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

10 authors.

Jiaqi LiDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Zhuomu HuDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Xinyu GuDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Yan WuHealth insurance office, Zhejiang Province People's Hospital, Hangzhou, Zhejiang, China.
Wei SunDepartment of Public Health, School of Public Health, Fudan University, Shanghai, China.
Yufei YangDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Yexin HuDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Yuxiao LiDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Hui ZhangDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.
Hao ZhangDepartment of Health Policy and Management, School of Public Administration, Hangzhou Normal University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The implementation of narrative medicine (NM) holds significant implications for multiple stakeholders in healthcare, necessitating investigation into clinicians' behavioral intention (BI) toward NM practice. This study employs the Theory of Planned Behavior (TPB) to identify predictors of Chinese clinicians' NM practice intentions, extending the theoretical framework through incorporation of external variables-Perceived Organizational Support (POS) and Perceived Informal Organizational Support (PIOS). Patients and methods: Data collected from 855 Chinese clinicians validated the theoretical model. The hybrid model of Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) demonstrated superior predictive accuracy, successfully capturing both linear associations and nonlinear interactions among variables. Phase I utilized SEM to identify intention predictors, while Phase II applied ANN to rank predictors' relative importance. Furthermore, conducting K-means clustering (optimal K = 4 determined by Elbow Method and Silhouette Coefficient) using ATT, SN, PBC, and PIOS as inputs to establish a "Psychological Motivation-Behavioral Intention" coupling profile. Chi-square tests examined distribution differences across organizational contexts. Results: Perceived behavioral control (PBC) was the strongest BI predictor, followed by PIOS, subjective norms (SN), and attitudes (ATT), with POS being non-significant. Four distinct profiles emerged: "Moderately Engaged, Moderately Intent" (Cluster 1), "Attitudinally Compliant, Structurally suppressed" (Cluster 2), "Cognitively Engaged, Moderately Intent" (Cluster 3), and "Fully Engaged, High-Intent" (Cluster 4). Distribution varied significantly across hospital levels ( Conclusion: ATT, SN, PBC, and PIOS positively influenced physicians' NM behavioral intention. The motivation-intention coupling profiles reveal heterogeneity in practice potential, offering theoretical-practical insights for targeted interventions.

Indexed as

behavioral intentionmotivational profilingnarrative medicineSEM-ANNtheory of planned behavior

Identifiers

PMID41625737
PMCPMC12851974

What OpenQuestion 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 OpenQuestion graph.