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© 2023. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Objective: To explore a mask fitness test based on self-efficacy and diversified training in the assessment system for nosocomial infection training.

Methods: From March 15 to April 5, 2022, 442 staff members (272 male and 170 female) of the Third People’s Hospital of Shenzhen who planned to enter the quarantine ward for secondary protection skill training assessment were selected. They comprised 56 doctors, 31 medical technicians, 72 nurses, and 283 property logistics staff. During the mask fitness test, a diversified training model based on self-efficacy was adopted to observe the passing status, the identification and selection of mask models, the method of mask-wearing, the fit between the mask and the face, and the changes in self-efficacy.

Results: In the assessment system for nosocomial infection training, the passing rate of the mask fitness test was correlated with the identification and selection of mask models, the method of wearing masks, the fit between the mask and the face, and the diversified training, and the differences were statistically significant (P < 0.05). The difference in the self-efficacy in the test takers between those before and after the mask fitness test was statistically significant (P < 0.05).

Conclusion: In the assessment system for nosocomial infection training, the mask fitness test based on self-efficacy and diversified training might improve the passing rate, the rate of correct mask model identification and selection, the rate of correct mask-wearing, and the degree of facial fit, thus to enhance the awareness of protection and improve self-efficacy.

Details

Title
Investigation of a Mask Fitness Test Based on Self-Efficacy and Diversified Training in the Assessment System for Nosocomial Infection Training
Author
Xiao, Bing; Lu-Lu, Sun; Yuan, Jing; Wan-Ling, Xiao; Liu, Ying; Man-Yuan, Cai; Qiao-Huo Liao
Pages
313-322
Section
Original Research
Publication year
2023
Publication date
2023
Publisher
Taylor & Francis Ltd.
e-ISSN
1178-6973
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2771446892
Copyright
© 2023. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.