Objective:To investigate the knowledge, attitudes, practices (KAP), and demands of medical staff in family physician teams regarding the application of artificial intelligence (AI) in contracted services, and to analyze the influencing factors.Methods:A cross-sectional study was conducted from June to July 2023. A total of 602 medical staff members from family physician teams in Shanghai Minhang District were selected as subjects. Data on demographics (age, gender, institution, position, education, work experience, household registration, professional title, marital status, fertility status) and KAP/demand regarding AI application in contracted services were collected using a self-designed questionnaire. Intergroup differences were analyzed. Multiple stepwise linear regression was employed to identify the main factors influencing AI application demand.Results:Among the 602 participants, 484 (80.4%) were aged 30-49 years, 466 (77.40%) were females, 559 (92.9%) held a bachelor′s degree or higher, and 505 (83.9%) had intermediate or senior professional titles. The awareness rate for knowledge, positive attitude rate, and practice implementation rate regarding AI application were 47.2% (284/602), 73.1% (440/602), and 32.1% (193/602), respectively. The mean scores for knowledge, attitude, and practice were 15.72±3.40, 18.34±3.41, and 14.60±3.89, respectively. Significant differences were found among the items within each KAP dimension (knowledge: F=7.688, P<0.001; attitude: F=5.106, P<0.001; practice: F=6.763, P<0.001). Within knowledge, item K3 (awareness of intelligent elderly monitoring devices) scored lowest (3.00±0.79), differing significantly from K1, K2, K4, and K5 (all P<0.05). Within attitude, item A5 (willingness to fully trust AI′s accuracy and convenience in contracted services) scored lowest (3.57±0.75), differing significantly from A3 and A4 (all P<0.05). Within practice, item P3 (increasing reliance on AI in daily contracted services) scored lowest (2.79±0.93), differing significantly from P1 and P2 (all P<0.05). KAP scores differed significantly across demographic subgroups. Knowledge scores differed significantly by age, gender, and marital status (all P<0.05). Attitude scores differed significantly by gender, household registration, and fertility status (all P<0.05). Practice scores differed significantly by gender, position, and marital status (all P<0.05). Regarding demand, the most frequently selected areas were follow-up services (28.74%, 173/602), data management (26.25%, 158/602), and data collection (25.42%, 153/602). Univariate analysis identified age, gender, education, professional title, fertility status, and KAP scores as significant factors influencing AI application demand (all P<0.05). Multiple stepwise linear regression revealed that older age ( t=3.905, P<0.001), female gender ( t=3.548, P<0.001), and higher practice scores ( t=-3.044, P=0.002) were significant predictors of greater AI application demand. Conclusions:Significant variations exist in the KAP levels regarding AI application among family physician team members. Gender, age, and practice behavior significantly influence demand. Tailored strategies for different subgroups, coupled with timely targeted training and practical exercises, are recommended to enhance the effective and widespread adoption of AI technology in family physician contracted services.

Objective:To investigate the knowledge, attitudes, practices (KAP), and demands of medical staff in family physician teams regarding the application of artificial intelligence (AI) in contracted services, and to analyze the influencing factors.Methods:A cross-sectional study was conducted from June to July 2023. A total of 602 medical staff members from family physician teams in Shanghai Minhang District were selected as subjects. Data on demographics (age, gender, institution, position, education, work experience, household registration, professional title, marital status, fertility status) and KAP/demand regarding AI application in contracted services were collected using a self-designed questionnaire. Intergroup differences were analyzed. Multiple stepwise linear regression was employed to identify the main factors influencing AI application demand.Results:Among the 602 participants, 484 (80.4%) were aged 30-49 years, 466 (77.40%) were females, 559 (92.9%) held a bachelor′s degree or higher, and 505 (83.9%) had intermediate or senior professional titles. The awareness rate for knowledge, positive attitude rate, and practice implementation rate regarding AI application were 47.2% (284/602), 73.1% (440/602), and 32.1% (193/602), respectively. The mean scores for knowledge, attitude, and practice were 15.72±3.40, 18.34±3.41, and 14.60±3.89, respectively. Significant differences were found among the items within each KAP dimension (knowledge: F=7.688, P<0.001; attitude: F=5.106, P<0.001; practice: F=6.763, P<0.001). Within knowledge, item K3 (awareness of intelligent elderly monitoring devices) scored lowest (3.00±0.79), differing significantly from K1, K2, K4, and K5 (all P<0.05). Within attitude, item A5 (willingness to fully trust AI′s accuracy and convenience in contracted services) scored lowest (3.57±0.75), differing significantly from A3 and A4 (all P<0.05). Within practice, item P3 (increasing reliance on AI in daily contracted services) scored lowest (2.79±0.93), differing significantly from P1 and P2 (all P<0.05). KAP scores differed significantly across demographic subgroups. Knowledge scores differed significantly by age, gender, and marital status (all P<0.05). Attitude scores differed significantly by gender, household registration, and fertility status (all P<0.05). Practice scores differed significantly by gender, position, and marital status (all P<0.05). Regarding demand, the most frequently selected areas were follow-up services (28.74%, 173/602), data management (26.25%, 158/602), and data collection (25.42%, 153/602). Univariate analysis identified age, gender, education, professional title, fertility status, and KAP scores as significant factors influencing AI application demand (all P<0.05). Multiple stepwise linear regression revealed that older age ( t=3.905, P<0.001), female gender ( t=3.548, P<0.001), and higher practice scores ( t=-3.044, P=0.002) were significant predictors of greater AI application demand. Conclusions:Significant variations exist in the KAP levels regarding AI application among family physician team members. Gender, age, and practice behavior significantly influence demand. Tailored strategies for different subgroups, coupled with timely targeted training and practical exercises, are recommended to enhance the effective and widespread adoption of AI technology in family physician contracted services.

Background: Nurses are the “front line” staff in most health systems and their contribution is recognised as essential in meeting development goals and delivering safe and effective care (ICN, 2007). Nurses are in high demand not only in developed countries but also in developing countries like Malaysia. However, more than 70% of Malaysian hospitals currently do not have adequate nursing staff. At least 174,000 nurses need to be trained by 2020 to meet WHO’s nurse-to-patient ratio of 1:200. The purpose of this study is to identify the main factors that influence the nursing students’ decision to choose nursing as their career. Method: A descriptive study guided by Self Determination Theory was used for this study. A 29- item questionnaire adapted from McCabe, Nowak and Mullen (2005) was distributed to all students in a nursing college (n=117). Results: The five main reasons for choosing nursing as a career were “ability to help others”, “training was provided on the job”, “ability to work closely with people”, “parental advice”, and “accommodation was provided while training”. The top three main domains that influenced the nursing students’ decision to choose nursing as their career include “travel opportunities of nursing”, “intrinsic attraction of nursing” and “immediacy of support on entry to nursing”. A total of 19 (0.2%) will not choose nursing if given a chance. The main reasons were “want to take another course”, “no time to spend with family” and “nursing is a stressful job”. Conclusion: The findings of this study provided valuable information regarding motivating factors which attract the current generation to join nursing. It is of concern that items representing nurses’ image were not rated highly
Nursing

Bone maintenance theory considers that the external load is the direct stimulating source of the bone remodeling. In this article, the method of experimental observation of self-repairing process of the bone defect and related results are introduced. Firstly, a hole was drilled in the rabbit thighbone so that the continuity of the bone was changed. Then bone defect model was established, and the thighbone data were obtained by using CT scanning, and the self-repairing process of bone defects caused by growth factor were observed and analyzed by MIMICS software. Finally, the relationship between volume changes of sclerotin was established, and scientific bases were provided for introducing the bionic topology optimization method to the remodeling process. The experimental results showed that the self-repairing of the each layer sclerotin of the young rabbits was faster than that of the adult ones under the same condition. In addition, the volume always changes contrarily between the spongy bone and enamel bone during the self-repairing process of bone defect.
Animals ; Bone Regeneration ; physiology ; Female ; Femoral Fractures ; physiopathology ; Femur ; injuries ; Fracture Healing ; physiology ; Male ; Osteogenesis ; physiology ; Rabbits

OBJECTIVETo determine the infective status and natural distribution of Xinjiang hemorrhagic fever (XHF; Crimean-Congo hemorrhagic fever, CCHF) in ticks, rodents and livestock in the Tarim Basin.

METHODSThe pathogenic materials of ticks or rodents' viscera and blood samples of sheep were inoculated into sucking mouse of 24 to 48-hour old. Materials with typical clinic symptoms were identified with RPHA and IFA. RT-PCR was taken to detect special S gene segment of Crimean-Congo hemorrhagic fever virus (CCHFV) in the objective material.

RESULTSAll the samples of ticks, rodents' viscera and blood samples of sheep from 21 counties (cities) in the Tarim Basin were divided into 422 groups and inoculated into sucking mouse at laboratory. 49 materials with typical clinic symptoms were obtained. The morbidity rate with typical clinic XHF was high in Bachu, Yuli, Yutian and Ruoqiang. There were 43 samples identified with RPHA with 6 positive samples and positive rate of 1.4%. The materials with positive RPHA were found in Yuli, Luntai and Yutian. 42 samples were identified with IFA and 13 positive samples with the positive rate of 3.1%. The positive materials of IFA were found in Bachu, Yuli, Minfeng, Luntai and Yutian. 32 samples were detected with RT-PCR and there were 31 samples with special S gene segment of CCHFV (329- 548 nt). The positive materials was widely distributed in Aksu, Awat, Bachu, Luopu, Yuli, Minfeng, Qiemo, Ruoqiang, Luntai and Yutian. The highest infective rate was in Hyalomma asiaticum kozlovi, and followed by sheep. S gene segment was detected in viscera of M. meridianus.

CONCLUSIONXHF relied on the river in the southern part of Xinjiang and distributed in the areas with Populus euphratica shrub in desert and oasis in the Tarim Basin. The main vector and host were Hyalomma asiaticum kozlovi. Livestock such as sheep, camel, L. yarkandensis, M. meridianus and Euchoreutes naso could serve as the deposited host of XHF.

Animals ; Animals, Domestic ; virology ; China ; epidemiology ; Hemorrhagic Fever Virus, Crimean-Congo ; genetics ; isolation & purification ; Hemorrhagic Fever, Crimean ; epidemiology ; transmission ; Humans ; Morbidity ; Polymerase Chain Reaction ; Rodentia ; virology ; Ticks ; virology

The purpose of this paper is to examine the pH adaptability range of two yeasts from our laboratory,and applied turbidimetry and Bradford methods to examine growth of Trichosporon asahii XJU-1 and Rhodotorula mucilaginosa XJU-1.It is shown that Trichosporon asahii XJU-1 grown between pH2.0 and pH13.0 and optimum pH is 8.0,whereas Rhodotorula mucilaginosa XJU-1 grown between pH3.0 and 12.0,optimum pH is 8.5.When turbidimetry was applied,it produced consensus results between pH4.0 and 10.0 with Bradford method.At the same time,produced senior distorted at pH