Nurses’ Job Satisfaction and Motivation: Patients’ and Nurses’ Perceptions

Background: High quality care is the main concern of healthcare administrators and it was found to be related to nurses’ job satisfaction and motivation. Purpose: The study aimed to compare patients’ and nurses’ perceptions of nurses’ job satisfaction and motivation. Method: In this cross-sectional study the participants were 315 released patients and 329 nurses from eight hospitals in northern Israel. The participants completed three questionnaires: demographics, nursing job satisfaction, and nursing job motivation. Results: Nurses perceived both job satisfaction and motivation higher than the patients. Duration of hospitalization and being accompanied by family, were positively correlated to patients’ perceptions of these variables. Conclusion/Implications for Practice: To optimize care quality, it is important to assess both patients’ and nurses’ perceptions of nurses’ job satisfaction and motivation. Such an assessment may shed light on the nursing experience and on the way that it is reflected in the patients’ experience. A system-wide process is needed to set a national standard for service quality while emphasizing strategies that promote recognition of nurses’ work, for example, compliments and acknowledgment. This could enhance nurses’ sense of work engagement, which, in turn, may increase the patients’ perception of nurses’ job satisfaction and motivation.

Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians

Objective To compare evaluations of depressive episodes and suggested treatment protocols generated by Chat Generative Pretrained Transformer(ChatGPT)-3 and ChatGPT-4 with the recommendations of primary care physicians.Methods Vignettes were input to the ChatGPT interface.These vignettes focused primarily on hypothetical patients with symptoms of depression during initial consultations.The creators of these vignettes meticulously designed eight distinct versions in which they systematically varied patient attributes(sex,socioeconomic status(blue collar worker or white collar worker)and depression severity(mild or severe)).Each variant was subsequently introduced into ChatGPT-3.5 and ChatGPT-4.Each vignette was repeated 10 times to ensure consistency and reliability of the ChatGPT responses.Results For mild depression,ChatGPT-3.5 and ChatGPT-4 recommended psychotherapy in 95.0%and 97.5%of cases,respectively.Primary care physicians,however,recommended psychotherapy in only 4.3%of cases.For severe cases,ChatGPT favoured an approach that combined psychotherapy,while primary care physicians recommended a combined approach.The pharmacological recommendations of ChatGPT-3.5 and ChatGPT-4 showed a preference for exclusive use of antidepressants(74%and 68%,respectively),in contrast with primary care physicians,who typically recommended a mix of antidepressants and anxiolytics/hypnotics(67.4%).Unlike primary care physicians,ChatGPT showed no gender or socioeconomic biases in its recommendations.Conclusion ChatGPT-3.5 and ChatGPT-4 aligned well with accepted guidelines for managing mild and severe depression,without showing the gender or socioeconomic biases observed among primary care physicians.Despite the suggested potential benefit of using atificial intelligence(AI)chatbots like ChatGPT to enhance clinical decision making,further research is needed to refine AI recommendations for severe cases and to consider potential risks and ethical issues.