Aim To investigate the relationship between job crafting and work engagement among hospital nurses. Background Job crafting is a relatively advanced job redesign concept, and few studies have investigated it among nurses. Methods This is a cross‐sectional study. A total of 636 nurses were recruited from one hospital in Saudi Arabia. Of them, 608 (95.6%) completed self‐administered, online questionnaires. The questionnaire assessed participants’ socio‐demographic data, job crafting and work engagement. Structured equation modelling (SEM) was used to examine the association between job crafting and work engagement. Results Data from 549 nurses were analysed. Most of the participants (85.1%) were females, and their mean scores of job crafting and work engagement were 3.54 ± 0.5 and 4.77 ± 1.1, respectively. The SEM revealed that job crafting accounted for 57% of the variance of work engagement. Conclusions Job crafting is a significant determinant of nurses’ work engagement. Implications for nursing management Supporting staff nurses to employ job crafting behaviours would positively improve their work engagement. This may include, but is not limited to, helping nurses to bargain a significance in their labour, reforming the work pattern in a manner that lines up with organisational objectives and employing an innovative managerial style.

Aim. To analyse the prevalence of self-care practices in T2D patients in KSA. Methods. The study was conducted in King Fahad Medical City (KFMC) in Saudi Arabia, and 385 patients were selected as samples. Data were collected using the Summary of Diabetes Self-Care Activities-Arabic (SDSCA) and consisted of 14 items related to self-care activities of T2D patients related to management and control of disease and four other aspects related to education and advice from healthcare members regarding management of T2D. Results. The self-care attributes including adherence to medication commitment activities ( M = 6.13 , SD = 1.25 ) were the most practised of all the domains. Glucose monitoring ( M = 4.15 , SD = 2.42 ) and foot care ( M = 3.28 , SD = 1.69 ) were at an average level, and adherence to the diet plan and exercise was found to be at a poor level ( M = 2.57 , SD = 1.73 and M = 2.13 , SD = 2.00 ) respectively. About 179 patients (74.3%) were found to be advised to follow a low-fat eating plan, and only 89 patients (36.9%) had received information concerning fruits and vegetables in their diet. More than 90% patients were found to be advised to strictly carry out exercise and blood sugar monitoring. Conclusion. It was found that adherence to self-care activities including diet, exercise, and foot care was relatively poor while intake of medication was strictly followed. The education provided by healthcare providers related to self-management attributes was found to be significant and had positive effects on the overall health and well-being of T2D patients.

The vast majority of the sample did not have a policy for lactating students, and almost half of the schools did not have designated space for milk expression accessible to students. Lactating students will likely encounter challenges in simultaneously sustaining breastfeeding and meeting their educational goals in these contexts. To meet the recommendation of the American Academy of Pediatrics of 6 months of exclusive breastfeeding and continued breastfeeding for 1 year or more, American colleges and universities must establish not only designated spaces for milk expression but also policies to support lactating students.

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Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.