Background: The COVID-19 outbreak negatively impacted pharmacists who provided basic medical services by inducing anxiety and depression, thus, leading to medical errors. Objective: This study aimed to investigate the...Background: The COVID-19 outbreak negatively impacted pharmacists who provided basic medical services by inducing anxiety and depression, thus, leading to medical errors. Objective: This study aimed to investigate the job burnout and satisfaction levels among hospital pharmacists during the period when China downgraded COVID-19 from a Category A disease to a Category B disease. Method: We selected pharmacists from several medical institutions in Yunnan Province as the subjects by using the general information questionnaire survey, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), and the Work Environment Scale-10 (WES-10). Results: After analyzing 461 questionnaires, the results showed that the age and marital status of the pharmacists displayed significant effects on their emotional exhaustion and sense of achievement, with younger pharmacists getting higher and lower scores for their tests on emotional exhaustion and sense of achievement, respectively (p Conclusion: Hence, it was concluded that the job burnout of pharmacists was at a low level during the period when China downgraded COVID-19 as a Category B disease from Category A.展开更多
Introduction: Nurses’ job satisfaction is referring to the level to which people feel that they are able to have an influence on their workplace. Nurse leaders should use a variety of empowerment strategies that are ...Introduction: Nurses’ job satisfaction is referring to the level to which people feel that they are able to have an influence on their workplace. Nurse leaders should use a variety of empowerment strategies that are important to nurses’ job satisfaction. Meanwhile, meaningful recognition for nurses is considered a powerful tool to enhance nurses’ sense of self-efficacy especially facing an emotional challenge that may affect their wellbeing. Aim: The aim of the studies is to analyze the importance of nurses’ recognition, and empowerment towards nurses’ job satisfaction at KFSH-D. Method: This study takes the form of a quantitative research methodology, and descriptive-analytical technique. A questionnaire used to gather data from registered nurses employed at King Fahad Specialist Hospital-Dammam (KFSH-D) about the structural empowerment and nurses’ recognition program impact on their job satisfaction. Following the collection of data, the descriptive statistic used to describe the personal characteristics of the respondents, while inferential statistics used to determine the statistical relationship existing between independent variable job satisfaction and the structural empowerment and nurses’ recognition program among the registered nurses at KFSH-D as dependent variables. Recommendation: The researcher only focuses on the in-patient units, future studies, are recommended to focus on every dimension and category level of units. Apart from that, when looking into job satisfaction and empowerment, to be more specific, the researcher could investigate another dimension by comparing age, and clinical working experience which may provide a depth of understanding of the contribution perception of structural empowerment. Conclusion: The findings of the studies reveal recognition and empowerment are assets to make nurses stay and increase their level of job satisfaction and task assignments.展开更多
Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification. However, African textual data, in general, have been studied very little using these methods. This article ...Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification. However, African textual data, in general, have been studied very little using these methods. This article notes the particularity of the data and measures the level of precision of predictions of naive Bayes algorithms, decision tree, and SVM (Support Vector Machine) on a corpus of computer jobs taken on the internet. This is due to the data imbalance problem in machine learning. However, this problem essentially focuses on the distribution of the number of documents in each class or subclass. Here, we delve deeper into the problem to the word count distribution in a set of documents. The results are compared with those obtained on a set of French IT offers. It appears that the precision of the classification varies between 88% and 90% for French offers against 67%, at most, for Cameroonian offers. The contribution of this study is twofold. Indeed, it clearly shows that, in a similar job category, job offers on the internet in Cameroon are more unstructured compared to those available in France, for example. Moreover, it makes it possible to emit a strong hypothesis according to which sets of texts having a symmetrical distribution of the number of words obtain better results with supervised learning algorithms.展开更多
文摘Background: The COVID-19 outbreak negatively impacted pharmacists who provided basic medical services by inducing anxiety and depression, thus, leading to medical errors. Objective: This study aimed to investigate the job burnout and satisfaction levels among hospital pharmacists during the period when China downgraded COVID-19 from a Category A disease to a Category B disease. Method: We selected pharmacists from several medical institutions in Yunnan Province as the subjects by using the general information questionnaire survey, the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), and the Work Environment Scale-10 (WES-10). Results: After analyzing 461 questionnaires, the results showed that the age and marital status of the pharmacists displayed significant effects on their emotional exhaustion and sense of achievement, with younger pharmacists getting higher and lower scores for their tests on emotional exhaustion and sense of achievement, respectively (p Conclusion: Hence, it was concluded that the job burnout of pharmacists was at a low level during the period when China downgraded COVID-19 as a Category B disease from Category A.
文摘Introduction: Nurses’ job satisfaction is referring to the level to which people feel that they are able to have an influence on their workplace. Nurse leaders should use a variety of empowerment strategies that are important to nurses’ job satisfaction. Meanwhile, meaningful recognition for nurses is considered a powerful tool to enhance nurses’ sense of self-efficacy especially facing an emotional challenge that may affect their wellbeing. Aim: The aim of the studies is to analyze the importance of nurses’ recognition, and empowerment towards nurses’ job satisfaction at KFSH-D. Method: This study takes the form of a quantitative research methodology, and descriptive-analytical technique. A questionnaire used to gather data from registered nurses employed at King Fahad Specialist Hospital-Dammam (KFSH-D) about the structural empowerment and nurses’ recognition program impact on their job satisfaction. Following the collection of data, the descriptive statistic used to describe the personal characteristics of the respondents, while inferential statistics used to determine the statistical relationship existing between independent variable job satisfaction and the structural empowerment and nurses’ recognition program among the registered nurses at KFSH-D as dependent variables. Recommendation: The researcher only focuses on the in-patient units, future studies, are recommended to focus on every dimension and category level of units. Apart from that, when looking into job satisfaction and empowerment, to be more specific, the researcher could investigate another dimension by comparing age, and clinical working experience which may provide a depth of understanding of the contribution perception of structural empowerment. Conclusion: The findings of the studies reveal recognition and empowerment are assets to make nurses stay and increase their level of job satisfaction and task assignments.
文摘Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification. However, African textual data, in general, have been studied very little using these methods. This article notes the particularity of the data and measures the level of precision of predictions of naive Bayes algorithms, decision tree, and SVM (Support Vector Machine) on a corpus of computer jobs taken on the internet. This is due to the data imbalance problem in machine learning. However, this problem essentially focuses on the distribution of the number of documents in each class or subclass. Here, we delve deeper into the problem to the word count distribution in a set of documents. The results are compared with those obtained on a set of French IT offers. It appears that the precision of the classification varies between 88% and 90% for French offers against 67%, at most, for Cameroonian offers. The contribution of this study is twofold. Indeed, it clearly shows that, in a similar job category, job offers on the internet in Cameroon are more unstructured compared to those available in France, for example. Moreover, it makes it possible to emit a strong hypothesis according to which sets of texts having a symmetrical distribution of the number of words obtain better results with supervised learning algorithms.
