Nursing staff teamwork and job satisfaction

kalisch bj , lee h & rochman m. (2010) Journal of Nursing Management   18, 938–947 Nursing staff teamwork and job satisfaction The aim of the present study was to explore the influence of unit characteristics, staff characteristics and teamwork on job satisfaction with current position and occupation.Teamwork has been associated with a higher level of job satisfaction but few studies have focused on the acute care inpatient hospital nursing team.This was a cross-sectional study with a sample of 3675 nursing staff from five hospitals and 80 patient care units. Participants completed the Nursing Teamwork Survey (NTS).Participants’ levels of job satisfaction with current position and satisfaction with occupation were both higher when they rated their teamwork higher ( P  < 0.001) and perceived their staffing as adequate more often ( P  < 0.001). Type of unit influenced both satisfaction variables ( P  < 0.05). Additionally, education, gender and job title influenced satisfaction with occupation ( P  < 0.05) but not with current position.Results of this present study demonstrate that within nursing teams on acute care patient units, a higher level of teamwork and perceptions of adequate staffing leads to greater job satisfaction with current position and occupation.Findings suggest that efforts to improve teamwork and ensure adequate staffing in acute care settings would have a major impact on staff satisfaction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79069/1/j.1365-2834.2010.01153.x.pd

Measurement of Human Walking Movements by Using a Mobile Health App: Motion Sensor Data Analysis

Background: This study presents a new approach to measure and analyze the walking balance of humans by collecting motion sensor data in a smartphone. Objective: We aimed to develop a mobile health (mHealth) app that can measure the walking movements of human individuals and analyze the differences in the walking movements of different individuals based on their health conditions. A smartphone\u27s motion sensors were used to measure the walking movements and analyze the rotation matrix data by calculating the variation of each xyz rotation, which shows the variables in walking-related movement data over time. Methods: Data were collected from 3 participants, that is, 2 healthy individuals (1 female and 1 male) and 1 male with back pain. The participant with back pain injured his back during strenuous exercise but he did not have any issues in walking. The participants wore the smartphone in the middle of their waistline (as the center of gravity) while walking. They were instructed to walk straight at their own pace in an indoor hallway of a building. The walked a distance of approximately 400 feet. They walked for 2-3 minutes in a straight line and then returned to the starting location. A rotation vector in the smartphone, calculated by the rotation matrix, was used to measure the pitch, roll, and yaw angles of the human body while walking. Each xyz-rotation vector datum was recalculated to find the variation in each participant\u27s walking movement. Results: The male participant with back pain showed a diminished level of walking balance with a wider range of xyz-axis variations in the rotations compared to those of the healthy participants. The standard deviation in the xyz-axis of the male participant with back pain was larger than that of the healthy male participant. Moreover, the participant with back pain had the widest combined range of right-to-left and forward-to-backward motions. The healthy male participant showed smaller standard deviation while walking than the male participant with back pain and the female healthy participant, indicating that the healthy male participant had a well-balanced walking movement. The walking movement of the female healthy participant showed symmetry in the left-to-right (x-axis) and up-to-down (y-axis) motions in the x-y variations of rotation vectors, indicating that she had lesser bias in gait than the others. Conclusions: This study shows that our mHealth app based on smartphone sensors and rotation vectors can measure the variations in the walking movements of different individuals. Further studies are needed to measure and compare walking movements by age, gender, as well as types of health problems or disease. This app can help in finding differences in gait in people with diseases that affect gait

Mobile Health App for Adolescents: Motion Sensor Data and Deep Learning Technique to Examine the Relationship Between Obesity and Walking Patterns

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health

Military personnel with chronic symptoms following blast traumatic brain injury have differential expression of neuronal recovery and epidermal growth factor receptor genes

Objective: Approximately one-quarter of military personnel who deployed to combat stations sustained one or more blast-related, closed-head injuries. Blast injuries result from the detonation of an explosive device. The mechanisms associated with blast exposure that give rise to traumatic brain injury (TBI), and place military personnel at high risk for chronic symptoms of post-concussive disorder (PCD), post-traumatic stress disorder (PTSD), and depression are not elucidated. Methods: To investigate the mechanisms of persistent blast-related symptoms, we examined expression profiles of transcripts across the genome to determine the role of gene activity in chronic symptoms following blast-TBI. Active duty military personnel with (1) a medical record of a blast-TBI that occurred during deployment (n = 19) were compared to control participants without TBI (n = 17). Controls were matched to cases on demographic factors including age, gender, and race, and also in diagnoses of sleep disturbance, and symptoms of PTSD and depression. Due to the high number of PCD symptoms in the TBI+ group, we did not match on this variable. Using expression profiles of transcripts in microarray platform in peripheral samples of whole blood, significantly differentially expressed gene lists were generated. Statistical threshold is based on criteria of 1.5 magnitude fold-change (up or down) and p-values with multiple test correction (false discovery rate \u3c0.05). Results: There were 34 transcripts in 29 genes that were differentially regulated in blast-TBI participants compared to controls. Up-regulated genes included epithelial cell transforming sequence and zinc finger proteins, which are necessary for astrocyte differentiation following injury. Tensin-1, which has been implicated in neuronal recovery in pre-clinical TBI models, was down-regulated in blast-TBI participants. Protein ubiquitination genes, such as epidermal growth factor receptor, were also down-regulated and identified as the central regulators in the gene network determined by interaction pathway analysis. Conclusion: In this study, we identified a gene-expression pathway of delayed neuronal recovery in military personnel a blast-TBI and chronic symptoms. Future work is needed to determine if therapeutic agents that regulate these pathways may provide novel treatments for chronic blast-TBI-related symptoms

Relationships among Parental Alcoholism, Sense of Belonging, Resilience and Depressive Symptoms in Korean People.

Although adult children of alcoholics (ACOA) are at risk for alcohol or other drug abuses and serious emotional problems, including depressive symptoms, “resilient” ACOAs grow up striving to adapt, survive and succeed under stressful conditions. Recent studies have reported that one of the key factors increasing resilience is sense of belonging, which also protects individuals from depressive symptoms. However, the relationships among depressive symptoms, sense of belonging, and resilience have rarely been studied in ACOAs. Therefore, this descriptive and comparative study between ACOAs and non-ACOAs aims to explore the relationships among parental alcoholism, sense of belonging, resilience, and depressive symptoms, especially among Korean people living in Midwestern cities of the States. Based on a literature review, a conceptual framework was proposed: Sense of belonging was suggested as a key factor enhancing ACOAs’ resilience, and resilience was defined as an acquired capacity to translate life adversities associated with parental alcoholism into desirable outcomes, i.e., having few or no depressive symptoms. Using a web-based survey, including the Beck Depression Inventory-II, the Sense of Belonging Instrument-Psychological, the Connor-Davidson Resilience Scale and family-related questionnaires, data from 206 Koreans and Korean Americans were collected. The mean age of the sample was 28.4 years (S.D. = 6.9), 40.2% were males, and 77.8% were undergraduate or graduate students. The mean BDI-II score was 8.9 (S.D. = 8.1), and nearly 15% were identified as ACOAs. Preliminary analysis results revealed significant relationships among parental alcoholism, depressive symptoms, sense of belonging, resilience, social support, family functioning, parental mental health problems, and domestic violence. Sense of belonging was the only mediator between parental alcoholism and depressive symptoms. Structural equation modeling confirmed sense of belonging as the most powerful and proximal factor resisting depressive symptoms, although resilience and social support also mediated some effects of parental alcoholism on depressive symptoms. Parental alcoholism had no direct effect on depressive symptoms. These findings provide important evidence for understanding both the psychological positive and risk factors of depressive symptoms. In addition, the findings will contribute to establishing fundamental knowledge, strengthened by cultural sensitivity, for health care providers to develop effective intervention programs for Korean ACOAs.Ph.D.NursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77732/1/mizbean_1.pd

Detection of Walking Features Using Mobile Health and Deep Learning

This study identifies seven human subjects’ walking features by training a deep learning model with sensor data. Using the proposed Mobile Health Application developed for collecting sensor data from an Android device, we collected data from human subjects with a history of mild traumatic brain injury. The sensors measure acceleration in m/s2 with respect to: the X, Y, and Z directions using an accelerometer, the rate of rotation around a spatial axis with a gyroscope, and nine parameters of a rotation vector with rotation vector components along the X, Y, Z axes using a rotation vector software-based sensor. We made a deep learning model using Tensorflow and Keras to identify the walking features of the seven subjects. The data are classified into the following categories: Accelerometer (X, Y, Z); Gyroscope (X, Y, Z); Rotation (X, Y, Z); Rotation vector (nine parameters); and a combination of the preceding categories. Each dataset was then used for training and testing the accuracy of the deep learning model. According to the Keras evaluation function, the deep learning model trained with Rotation vector data shows 99.5% accuracy for classifying walking characteristics of subjects. In addition, the ability of the model to accurately classify the characteristics of subjects’ walking with all datasets combined is 99.9%

In Situ Visualization of Localized Surface Plasmon Resonance-Driven Hot Hole Flux

Nonradiative surface plasmon decay produces highly energetic electron–hole pairs with desirable characteristics, but the measurement and harvesting of nonequilibrium hot holes remain challenging due to ultrashort lifetime and diffusion length. Here, the direct observation of LSPR-driven hot holes created in a Au nanoprism/p-GaN platform using photoconductive atomic force microscopy (pc-AFM) is demonstrated. Significant enhancement of photocurrent in the plasmonic platforms under light irradiation is revealed, providing direct evidence of plasmonic hot hole generation. Experimental and numerical analysis verify that a confined |E|-field surrounding a single Au nanoprism spurs resonant coupling between localized surface plasmon resonance (LSPR) and surface charges, thus boosting hot hole generation. Furthermore, geometrical and size dependence on the extraction of LSPR-driven hot holes suggests an optimized pathway for their efficient utilization. The direct visualization of hot hole flow at the nanoscale provides significant opportunities for harnessing the underlying nature and potential of plasmonic hot holes11Nsciescopu

Direct Imaging of Surface Plasmon-Driven Hot Electron Flux on the Au Nanoprism/TiO2

Direct measurement of hot electron flux from a plasmonic Schottky nanodiode is important for obtaining fundamental insights explaining the mechanism for electronic excitation on a surface. Here, we report the measurement of photoinduced hot electrons on a triangular Au nanoprism on TiO2 under incident light with photoconductive atomic force microscopy (pc-AFM), which is direct proof of the intrinsic relation between hot electrons and localized surface plasmon resonance. We find that the local photocurrent measured on the boundary of the Au nanoprism is higher than that inside the Au nanoprism, indicating that field confinement at the boundary of the Au nanoprism acts as a hot spot, leading to the enhancement of hot electron flow at the boundary. Under incident illumination with a wavelength near the absorption peak (645 nm) of a single Au nanoprism, localized surface plasmon resonance resulted in the generation of a higher photoinduced hot electron flow for the Au nanoprism/TiO2, compared with that at a wavelength of 532 nm. We show that the application of a reverse bias results in a higher photocurrent for the Au nanoprism/TiO2, which is associated with a lowering of the Schottky barrier height caused by the image force. These nanoscale measurements of hot electron flux with pc-AFM indicate efficient photon energy transfer mediated by surface plasmons in hot electron-based energy conversion. © 2019 American Chemical Societ

The Development And Testing Of The Nursing Teamwork Survey

Background: There is a lack of an acceptable, reliable, and valid survey instruments to differentiate levels of nursing teamwork on inpatient units in acute care facilities. Objective: The aim of this study was to test the psychometric soundness of the Nursing Teamwork Survey (NTS). Methods: The survey was administered to 1,758 inpatient nursing staff members using the NTS (return rate = 56.9%), and measures of content, predictive (concurrent), and construct (factorial, contrast, and convergent) validity were completed. Results: Content validity was established by a panel of experts. Concurrent validity showed a significant correlation between teamwork scores and an imbedded question related to overall satisfaction with teamwork (r = .633, p \u3c .001). The exploratory factor analysis on a random half of the sample predicted a 33-item five-factor solution, whereas the confirmatory factor analysis on the remaining half of the sample confirmed the factor structure (comparative fit index = .884, root mean square error of approximation = 0.055, standardized root mean square residual = 0.045). Contrast validity showed that staff in a non-inpatient unit did not answer the questions in the same way (rWG(J) = .25) as the inpatient unit staff (rWG(J) \u3e .90). Convergent validity of the teamwork tool was measured by correlating the Teamwork subscale of the Safety Attitudes Questionnaire with the NTS (r = .76, p \u3c .01). The NTS had good test-retest reliability (r = .92 for overall 33 items; r = .77 to.87 for the five subscales) and internal consistency (α = .94 for overall items; α = .74 to.85 for the subscales). Aggregation of individual-level responses to the unit level was supported by intraclass correlation coefficient 1 = .16 (p \u3c .001), intraclass correlation coefficient 2 = .9 (p \u3c .001), and mean rWG(J) = .98. Discussion: The NTS was demonstrated to have good psychometric properties. Further NTS research should include testing the tool in hospitals with varying characteristics and exploring the links to clinical and operational outcomes. Copyright © 2010 Lippincott Williams & Wilkins

Nanotribological Effect of Water Layers Intercalated between Exfoliated MoS2 and Mica

© 2020 American Chemical Society. When the two-dimensional material molybdenum disulfide (MoS2) is exfoliated on a hydrophilic substrate such as mica, a layer of water is intercalated at the interface at a relative humidity over 20%. This intercalated water layer increases friction at a microscopic scale by providing an additional excitation channel. Using atomic force microscopy, we quantitatively examined various frictional effects from the intercalated water layer. A general tendency of friction dependence on the number of MoS2 and water layers followed the case of graphene exfoliated on mica, despite different structure, layer thickness, hydrophilicity, and water growth mode in the intercalated water layer. These phenomena reveal a universal trend of frictional behavior in confined water, indicating that the physical and electronic properties of the atomic layer covering intercalated water layers do not play an important role11sci