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10 December 2023, Volume 30 Issue 23
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Journal of Nursing. 2023, 30(23): 1-5. https://doi.org/10.16460/j.issn1008-9969.2023.23.001Objective To construct and verify the prediction scoring model for lactation risk of mothers experiencing premature birth, and provide reference for early identification of risk group. Methods Meta-analysis was used to analyze the factors of lactation risk in mothers of premature infants, and the model was established using the natural logarithm of the overall risk level for each risk factor as the coefficient, and the natural logarithm of the ratio between the failure rate and non-failure rate of lactation among mothers of preterm infants as the model coefficient. The risk factors were then assigned scores based on their respective coefficient values to construct the model. The data of 112 mothers experiencing premature birth from March to September, 2022 were collected, and the predictive performance of the model was analyzed. Results The model was constructed with logit(P)=-0.072+0.389 age+0.452 gestational hypertension+1.008 gestational diabetes+0.434 postpartum depression+0.538 lactation phase II start-up delay+0.607 daily milking frequency+0.515 milk opening time+0.445 lack of sleep. The area under the ROC curve of the model was 0.900 (95%CI: 0.841~0.958); the Jordan index 0.717, and the critical value 2.070. The probability value of the model was 88.8%, with sensitivity and specificity of 0.889 and 0.828 respectively. The verification results of the model showed that the score ranged from 0 to 113, and a score above 55.5 indicated a high risk of lactation. The area under ROC curve was 0.900 (95%CI:0.842~0.958), and the Jordan index 0.717, with the sensitivity and specificity of 0.889 and 0.828. The positive predictive value was 82.8%, and the negative predictive value 82.8%. Conclusion The prediction scoring model of lactation risk of mothers experiencing premature birth based on meta-analysis has good prediction efficiency, and can be used for lactation screening of premature mothers and establishment of risk groups.
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Journal of Nursing. 2023, 30(23): 6-11. https://doi.org/10.16460/j.issn1008-9969.2023.23.006Objective To investigate the current status of reversible cognitive frailty among the elderly in medical-nursing combined care institutions and analyze its influencing factors. Methods From February to May 2023, convenience sampling was used to select 438 elderly people from 5 medical-nursing integrated care institutions in Hefei as the subjects. General information questionnaire, Fried Frailty Phenotype, Montreal Cognitive Assessment, Clinical Dementia Rating Scale, Single Item Subjective Cognitive Decline, Mini Nutritional Assessment (MNA) and the Geriatric Depression Scale (SDS) were used for the investigation. Binary Logistic regression was used to analyze the influencing factors of reversible cognitive frailty in the elderly. Results The incidence of reversible cognitive frailty was 29.0% in the elderly in medical-nursing integrated care institutions. Binary Logistic regression analysis showed that age(OR=3.243, 9.832), number of chronic diseases（OR=2.700, 4.508）and depression（OR=3.681）were the risk factors for reversible cognitive frailty in the elderly in medical-nursing integrated care institutions（all P<0.05）. Education background（OR=0.448, 0.387, 0.316）, self-rated sleep quality（OR=0.475）, nutritional status（OR=0.298,0.106）and exercise（OR=0.496,0.483）were its protective factors（all P<0.05）. Conclusion The incidence of reversible cognitive frailty was 29.0% in the elderly in medical-nursing integrated care institutions. It is crucial to pay closer attention to specific subgroups within this population, including those who are advanced in age, have lower levels of education, multiple chronic diseases, poor self-rated sleep quality, limited physical exercise, malnutrition, and depression. Targeted intervention strategies should be implemented to mitigate or prevent the occurrence and progression of reversible cognitive frailty.
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Journal of Nursing. 2023, 30(23): 18-22. https://doi.org/10.16460/j.issn1008-9969.2023.23.018Objective To construct the indicators of entrustable professional activities (EPAs) for new nurses. Methods In this study, an expert correspondence questionnaire was initially developed through literature review and group discussion, and 2 rounds of expert correspondence were conducted from December 2022 to March 2023 using the Delphi method with 20 experts in the field of nursing education. Results In the 2 rounds of expert correspondence, the positive coefficient of the experts was 90.9% and 100%, the coefficient of expert authority both 0.883, and the coefficient of expert harmonization 0.142 and 0.140, respectively (all P<0.05). Ten indicators of EPAs for new nurses and their content descriptions were constructed, and the expected entrustable levels of new nurses at different stages were determined. Conclusion The indictors of EPAs for new nurses based on the Delphi method are scientific and reliable, and can provide reference for evaluating the competency of new nurses.
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Journal of Nursing. 2023, 30(23): 23-28. https://doi.org/10.16460/j.issn1008-9969.2023.23.023Objective To construct the nursing unit construction and management standard of chest pain center, and to provide reference for the nursing construction of chest pain center. Methods The draft of the standard was constructed after literature review and qualitative interview, and the draft was revised after two rounds of expert letter consultation with 25 experts at national level by Delphi method. Results The positive coefficient of the two rounds of expert consultation was 86.2% and 100%; the expert authority coefficient 0.922 and 0.906, and the Kendall coordination coefficient (W) 0.184 and 0.214, respectively (P<0.001). The final standards for the construction and management of nursing units of chest pain center included 5 general principles, 5 first-level indicators (organization construction, environmental settings, equipment and drug configuration, training and assessment, management and standardization), 14 second-level, and 52 third-level indicators. Conclusion The constructed nursing unit construction and management standard in chest pain center is scientific and operable, which can provide clinical guidance for the development of nursing unit in chest pain center.
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Journal of Nursing. 2023, 30(23): 29-34. https://doi.org/10.16460/j.issn1008-9969.2023.23.029Objective To translate the Ethical Leadership at Work Questionnaire (ELW) in nurses and to test its reliability and validity. Methods Based on Brislin's translation model, the Chinese version of ELW was developed through translation, back-translation, cultural adaptation, and pre-survey. A sample of 536 nurses from four tertiary grade-A hospitals in Zhengzhou was conveniently selected from January to March 2023 for a questionnaire survey to test the reliability and validity of the Chinese version of ELW. Results The Chinese version of ELW consisted of 37 items in 6 dimensions: human-centeredness, fairness and equity, power sharing and sustainable development, ethical guidance, honesty and trustworthiness, and clear responsibilities. Exploratory factor analysis extracted six common factors, with a cumulative variance contribution of 59.906%. Confirmatory factor analysis supported the hypothesis of the first-order six-factor model, and the evaluation indexes of model fitness reached statistical standard. The item level content validity index of the scale was 0.830~1.000, and the average scale level content validity index 0.959. It had a Cronbach's alpha coefficient of 0.958, a split-half reliability of 0.891, and a test-retest reliability of 0.957. Conclusion The Chinese version of ELW demonstrates good reliability and validity in assessing nurses'perceived ethical leadership within Chinese cultural context.
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Journal of Nursing. 2023, 30(23): 44-49. https://doi.org/10.16460/j.issn1008-9969.2023.23.044Objective To systematically evaluate the risk prediction model for deep vein thrombosis in hospitalized patients based on machine learning. Methods We conducted literature research in PubMed, Embase, CHINHAL, Cochrane Library, Web of Science, CNKI, and Wanfang databases for literature on risk prediction models for deep vein thrombosis in hospitalized patients constructed by machine learning. The search period spanned from the inception to March 2023. Two researchers completed literature screening and data extraction independently, and used predictive models to construct a research data extraction and quality evaluation checklist (CHARMS) to evaluate the quality of the included literature and screened high-quality literature for discussion. Results Totally 11 high-quality studies were collected, including 28 machine learning models, with an area under the ROC curve ranging from 0.710 to 0.976. Laboratory indicators such as age, VTE history, length of hospital stay, medication history, and D-dimer were are the main predictive factors. Conclusions Risk prediction models constructed using machine learning can accurately identify the risk of DVT events in hospitalized patients, and its predictive performance is superior to traditional risk prediction models. The available literature on the topic exhibits a low overall risk of bias, however, the applicability level of the prediction model is considered average.
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Journal of Nursing. 2023, 30(23): 50-56. https://doi.org/10.16460/j.issn1008-9969.2023.23.050
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Journal of Nursing. 2023, 30(23): 57-61. https://doi.org/10.16460/j.issn1008-9969.2023.23.057Objective To explore the influencing factors and provide reference for the prevention and intervention of cognitive decline in elderly people with subjective cognitive decline (SCD) by comparing cognitive function predictive models based on machine learning. Methods With the data of China Health and Retirement Longitudinal Study (CHARLS) in 2018, 2,969 elderly people with SCD were screened out. The least absolute shrinkage and selection operator (LASSO) regression, support vector regression (SRV), and random forest (RF) regression were used to construct predictive models for cognitive function of the elderly with SCD. The influencing factors of cognitive function were extracted based on the optimal predictive model. Results Among the three models, the one constructed by RF regression demonstrated the highest accuracy in predicting cognitive function in older adults experiencing SCD (R2=0.864, MAE=1.988, MSE=5.879). The factors affecting the cognitive function of the elderly with SCD can be ranked in order of importance as follows: physical dysfunction, age, the total score of depression, self-assessment of health, education background, the total score of entertainment, cleanliness, nap time, total score of IADL, with broadband or not. Conclusion Cognitive function predictive model constructed using RF regression demonstrates superior performance compared to models constructed using LASSO regression and SVR. By integrating the specific circumstances and risk factors of older adults, clinical professionals can develop personalized and multidimensional intervention plans that address factors such as learning, leisure activities, daily routines, nap time, and internet usage, so as to prevent cognitive function decline in older adults experiencing SCD.
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Journal of Nursing. 2023, 30(23): 73-78. https://doi.org/10.16460/j.issn1008-9969.2023.23.073Objective To understand the psychological experience and benefit finding in young breast cancer patients during the convalescence. Methods Purposive sampling was used to recruit 12 young breast cancer patients from a tertiary grade-A hospital in Zhanjiang, Guangdong Province. Semi-structured interviews were conducted and Colaizzi 7-step analysis was used to sort out the data. Results Four themes were extracted from the psychological experience during the rehabilitation, including impact of diagnosis and reluctant acceptance, pain and patience of treatment, need for care and encouragement during rehabilitation, and gratitude and post-traumatic growth after cancer. Five themes were extracted from the benefit finding, including personality change, changes in the relationships, change in the life, the meaning of illness, and gratitude and dedication. Conclusion The young breast cancer patients are experiencing negative emotions and pressures brought by the disease, as well as benefit discovery and positive growth. To enhance the physical and mental well-being of young breast cancer patients, healthcare providers should prioritize fostering positive psychological experiences and facilitating the discovery of benefits.
