📄 medicine

Network Analysis of Teaching Self-Efficacy and Tacit Knowledge Sharing among Clinical Nursing Preceptors: A Multicenter Cross-Sectional Study

This multicenter cross-sectional study utilized network analysis to map the item-level associations between teaching self-efficacy and tacit knowledge sharing among 1,221 clinical nursing preceptors in China, identifying key central and bridge nodes—such as creating learning opportunities and externalizing experience—that can inform targeted preceptor training strategies.

Original authors: Jie Li, Zhining Liu, Chengwei Xu, Ming Jin, Zhiqiang Wang

Published 2026-07-10

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Jie Li, Zhining Liu, Chengwei Xu, Ming Jin, Zhiqiang Wang

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the world of clinical nursing preceptors (the experienced nurses who teach new students) not as a list of separate skills, but as a giant, bustling city map. In this city, every single teaching habit and every piece of hidden "street-smart" knowledge is a building. For a long time, researchers just looked at the city's total population count. But this study decided to draw the actual roads connecting every building to see how the city really works.

The City Map: 32 Buildings and 234 Roads
The researchers surveyed 1,221 clinical nursing preceptors from four hospitals in Guangyuan, Sichuan Province, between February and June 2026. They asked these teachers to rate themselves on 32 different items (the buildings). Some buildings were about "Teaching Self-Efficacy" (how confident a teacher feels), and others were about "Tacit Knowledge Sharing" (sharing the unspoken, tricky stuff that you can't find in a textbook, like how to handle a difficult patient conversation or a specific procedure).

They found that these buildings are all connected by 234 roads. The map isn't random; it's a tightly knit network where changing one building affects its neighbors.

The Strongest Roads
The study found the busiest, strongest roads on the map. The most powerful connection was between two specific "Tacit Knowledge" buildings:

Exchanging work-related experience with other nurses.
Sharing information on key procedural techniques when asked.

These two are so linked that they act like a super-highway. It suggests that when a preceptor shares a "how-to" for a medical procedure, they are almost always sharing the story and experience behind it at the same time.

The Most Important Buildings (The "Central" Nodes)
Some buildings are so central that if you want to improve the whole city, you have to start there. The study identified three "super-buildings" with the highest strength centrality (meaning they have the most direct connections to everything else):

Creating learning opportunities to help interns adapt to the clinical environment.
Optimizing clinical teaching processes based on how interns are doing on their assessments.
Promoting the integration of clinical practice, teaching, and research through multidisciplinary collaboration.

Think of these as the city's main power plants. If you want to boost the whole network, you focus on helping teachers create better learning spots, adjusting their teaching based on student feedback, and getting different medical teams to work together.

The Bridges: Connecting Two Neighborhoods
The city has two main neighborhoods: "Confidence" (Self-Efficacy) and "Hidden Knowledge" (Tacit Knowledge). The study found specific "bridge buildings" that connect these two neighborhoods. The strongest bridges were:

Expressing ideas during training or lectures.
Privately guiding nurses on communication skills when things go wrong.
Sharing work-related know-how with other nurses.

This suggests that the way a teacher talks about their ideas in a formal setting (like a lecture) is the secret bridge that connects their internal confidence to their ability to share their hidden, practical wisdom.

What the Map Does NOT Show (And What It Rules Out)
It is crucial to understand what this map doesn't tell us.

It's not a cause-and-effect machine: The study explicitly states that because they took a snapshot of the city at one moment in time (a cross-sectional study), they cannot prove that building a new "learning opportunity" building causes better knowledge sharing. It only shows they are connected. The authors warn that these findings are "exploratory associations," not proof of what causes what.
Experience doesn't automatically change the map: You might think that a teacher with 10 years of experience would have a totally different city map than someone with 2 years. The study explicitly ruled this out. The network structure did not differ based on how many years of precepting experience the teachers had. Just having more years on the job doesn't necessarily rearrange the roads in the network.
Age and Training do change the roads: However, the map did look different for teachers under 35 compared to those 35 and older. It also looked different for those who attended training frequently versus those who did so less frequently. This suggests that age and training exposure might change how the buildings connect, even if the total number of roads stays the same.

The Numbers and the "Ceiling" Effect
The teachers reported very high scores overall.

The average Teaching Self-Efficacy score was 167.78 ± 38.44 (out of a possible 216).
The average Tacit Knowledge Sharing score was 36.96 ± 4.65 (out of a possible 40).

Because the scores were so high, the researchers noted a "ceiling effect," meaning many teachers were already near the top of the scale. This might make it harder to see small differences in the data. Also, the study used a statistical method called regularized partial-correlation to ensure the roads they drew were real and not just random noise. They checked the stability of their map using 1,000 bootstrap samples (a way of testing the map's durability) and found the main connections were stable (a stability coefficient of 0.60).

The Bottom Line
This study suggests that to improve clinical nursing education, we shouldn't just look at the total score of a teacher's confidence or knowledge. Instead, we should look at the specific roads connecting them. The data hints that focusing on creating learning opportunities, using feedback to adjust teaching, and sharing hidden know-how might be the best places to start. But remember, this is a map of connections, not a blueprint of cause-and-effect. The authors suggest these ideas as "candidate priorities" for future training, but they emphasize that we need more studies to prove that changing these specific roads will actually fix the traffic jams in the city.

Technical Summary: Network Analysis of Teaching Self-Efficacy and Tacit Knowledge Sharing among Clinical Nursing Preceptors

Problem Statement
Clinical nursing preceptors are critical for translating classroom knowledge into clinical competence. While teaching self-efficacy (TSE) and tacit knowledge sharing (TKS) are recognized as vital components of precepting capacity, existing research has largely relied on total-score correlations, regression, or latent class analysis. These traditional methods fail to reveal conditional, item-level associations or identify specific "key nodes" that might serve as leverage points for intervention. Consequently, the structural interplay between specific teaching confidence items and specific knowledge-sharing behaviors remains unclear, limiting the precision of future preceptor training programs.

Methodology
This study employed a multicenter cross-sectional design involving 1,221 clinical nursing preceptors from four hospitals in Guangyuan, Sichuan Province, China (data collection: February–June 2026).

Measures: Data were collected using the Clinical Teacher Tacit Knowledge Sharing Scale (CTKS; 8 items) and the Chinese version of the Clinical Nursing Teachers' Teaching Self-Efficacy Scale (CSE; 24 items).
Analytical Approach: The study utilized Network Analysis (NA) to model the relationships between the 32 scale items (nodes).
- Network Estimation: A regularized partial-correlation network was estimated using a Gaussian Graphical Model (GGM) with Graphical Least Absolute Shrinkage and Selection Operator (LASSO) regularization and an Extended Bayesian Information Criterion (EBIC) tuning parameter ( $\gamma = 0.5$ ). This approach controls for other nodes to identify direct conditional associations.
- Stability and Accuracy: Edge-weight accuracy was assessed via 1,000 bootstrap samples (95% confidence intervals). Centrality stability was evaluated using case-dropping bootstrap analysis, calculating the Correlation Stability (CS) coefficient.
- Centrality Metrics: Strength, closeness, and betweenness centrality were calculated to identify central nodes. Bridge strength was computed to identify nodes connecting the CSE and CTKS communities.
- Group Comparisons: Network Comparison Tests (NCT) were conducted to examine structural and global strength invariance across age groups (<35 vs. ≥35), precepting experience (<5 vs. ≥5 years), and training frequency.

Key Results

Descriptive Statistics: Participants reported high levels of both constructs (Mean CSE: 167.78 ± 38.44; Mean CTKS: 36.96 ± 4.65). Reliability was high (Cronbach's $\alpha$ > 0.90 for all scales and dimensions).
Network Structure: The network comprised 32 nodes and 234 non-zero edges.
- Strongest Edge: The strongest connection (weight = 0.455) existed between CTKS6 ("exchange work-related experience with nurses") and CTKS8 ("share information on key procedural techniques").
- Other Strong Edges: High-weight edges were observed between CSE6 and CSE7 (feedback-based teaching adjustment) and between CSE10 and CSE11 (curriculum-practice alignment).
Centrality Findings:
- Strength Centrality: The highest strength centrality nodes were CSE2 ("create learning opportunities"), CSE16 ("promote integration of practice, teaching, and research via multidisciplinary collaboration"), and CSE6 ("optimize teaching processes based on assessment results").
- Bridge Strength: The strongest bridge nodes connecting TSE and TKS were CTKS3 ("express ideas during training/lectures"), CTKS2 ("privately guide nurses in communication"), and CTKS1 ("share work-related know-how").
Stability: The correlation stability coefficient for strength centrality was 0.60, indicating good stability.
Group Differences: Network structures differed significantly by age ( $p=0.010$ ) and training frequency ( $p=0.010$ ), but not by precepting experience ( $p=0.178$ ). Global strength did not differ significantly across any groups.

Key Contributions

Item-Level Granularity: Unlike previous studies using aggregate scores, this research maps the conditional associations between specific teaching behaviors and knowledge-sharing actions, revealing that TSE and TKS form an interconnected item-level network rather than isolated constructs.
Identification of Candidate Priorities: The study identifies specific nodes (e.g., creating learning opportunities, feedback-informed adjustment, multidisciplinary collaboration) that act as central hubs in the precepting competence network.
Bridge Mechanisms: It highlights specific behaviors (expressing ideas in formal learning, communication guidance, sharing know-how) that serve as bridges between self-efficacy and tacit knowledge sharing, suggesting pathways for externalizing implicit knowledge.
Stratified Insights: The findings suggest that network structures vary by career stage (age) and training exposure, implying that a "one-size-fits-all" training approach may be insufficient.

Significance and Claims
The paper posits that these findings offer exploratory item-level evidence to inform future preceptor training and clinical nursing education quality improvement. The authors claim that the identified central and bridge nodes may represent candidate priorities for training interventions, specifically focusing on:

Learning opportunity creation.
Feedback-informed teaching adjustment.
Multidisciplinary collaboration.
Tacit knowledge externalization.

Limitations and Modesty of Claims
The authors explicitly state that due to the cross-sectional self-report design, the identified network edges represent conditional associations rather than causal evidence. They caution against interpreting central nodes as definitive intervention targets without longitudinal or experimental validation. The study acknowledges potential common method bias (indicated by Harman's single-factor test) and the clustering of scores toward the upper range, which may influence edge-weight estimates. Furthermore, the use of convenience sampling within a single region limits generalizability to other hospital levels or regions. The authors conclude that the identified nodes should be viewed as potential training priorities that require further testing in intervention studies.

Technical Summary: Network Analysis of Teaching Self-Efficacy and Tacit Knowledge Sharing among Clinical Nursing Preceptors

More like this