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. 2025 May 20;25(1):735.
doi: 10.1186/s12909-025-07302-8.

Knowledge, attitudes, practices, and burnout related to respiratory support among healthcare professionals in central China: a structural equation modeling study

Tao Cui #  1 , Jie Lui #  1 , Bin Chen  1 , Chuangwei Yu  1 , Yunli Hu  1 , Chuanfei Bao  1 , Shuguang Zhao  2
Affiliations

Knowledge, attitudes, practices, and burnout related to respiratory support among healthcare professionals in central China: a structural equation modeling study

Tao Cui et al. BMC Med Educ. .

Abstract

Background: Burnout, marked by emotional exhaustion and reduced clinical performance, may impair the effective application of noninvasive respiratory support (NIRS) and timely transition to invasive methods, potentially affecting patient outcomes. This study aims to identify the impact of burnout on the knowledge, attitudes, and practices (KAP) of healthcare professionals in the application of respiratory support, and further explore how other factors may influence these areas.

Method: A cross-sectional study was conducted from November 15, 2023, to December 14, 2023, at multiple hospitals in central China, involving key departments such as emergency, respiratory, cardiology, and critical care. Demographic information, alongside scores measuring KAP was gathered through the dissemination of questionnaires. Knowledge was assessed using a scoring system (range: 0-24), while attitude and practice were measured using 5-point Likert scales, with score ranges of 8-40 and 8-56, respectively. The Chinese version of the Maslach Burnout Inventory General Survey (MBI-GS) was used to assess occupational burnout.

Results: A total of 517 valid questionnaires were enrolled, including 284 (54.9%) nurses, and 269 (52%) had worked for less than 10 years. The median scores for knowledge, attitude, practice, and burnout were 20, 26, 38, and 40, respectively. Participants from private hospitals exhibited burnout scores higher than 50. Burnout was negatively correlated with both attitude (r = -0.289) and practice (r = -0.206). Multivariate logistic regression showed that practice, as the dependent variable, was independently associated with a knowledge score below 20 (OR = 0.441, 95% CI: [0.297, 0.657]), an attitude score below 26 (OR = 0.493, 95% CI: [0.335, 0.724]), and burnout scores below 40 (OR = 0.539, 95% CI: [0.364-0.796]) were independently associated with practice. Age above 40 years (OR = 0.470, 95% CI: [0.264, 0.837]), being a nurse (OR = 0.627, 95% CI: [0.424, 0.928]), and lack of recent training in respiratory support (OR = 0.590, 95% CI: [0.403, 0.866]) were also associated with lower practice scores.

Conclusions: Healthcare professionals had sufficient knowledge, positive attitudes, and proactive practices regarding the application of respiratory support. However, the impact of burnout must not be overlooked, even for those scoring below the threshold (50 points), as burnout can still significantly affect clinical performance. Healthcare institutions should prioritize continuous education and training programs focusing on respiratory support, especially for high stress environment professionals, to enhance clinical practice and patient outcomes.

Clinical trial number: not applicable.

Keywords: Attitude; Healthcare professional; Knowledge; Practice; Respiratory support.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Ethics Committee of the Taihe County People’s Hospital, and informed consent was obtained from the participants for the study through an online questionnaire. I confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The Structural Equation Model (SEM) Before and After Model Adjustment; (A) Before Model Adjustment; (B) After Model Adjustment. Rectangle shows observed variables, ellipses indicate potential variables, and circles represent residual terms
See this image and copyright information in PMC

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