Data on perceived excessive workload on faculty members׳ commitment.
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ABSTRACT:
SUBMITTER: Oludayo OA
PROVIDER: S-EPMC6138980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The main objective of this paper is to present the data set which depicts faculty commitment and effectiveness of job responsibilities in a changing world and the moderating role of the university׳s support system. The population of the study consisted all the 1912 Faculty members of six selected private universities in Southwest, Nigeria [5]. The sample size determination formula by [5] was adopted, resulting in the selection of 400 respondents to whom a structured questionnaire was administered accordingly. Only 343 copies of the questionnaire were valid and used for this data set. Structural equation modeling, which combines factor analysis and multiple regression, was used to present the structural relationship between dependent and independent variables. When the data is analysed, it will help to determine the degree of relationship and the strength of significance between the observed variables and the latent constructs.
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Project description:Previous research has suggested that personality traits of the Five Factor Model play a role in worker's response to workload. The aim of this study was to investigate the association of personality traits of first responders with their perceived workload in real-life tasks. A flying column of 269 police officers completed a measure of subjective workload (NASA-Task Load Index) after intervention tasks in a major public event. Officers' scores on a measure of Five Factor Model personality traits were obtained from archival data. Linear Mixed Modeling was used to test the direct and interaction effects of personality traits on workload scores once controlling for background variables, task type and workload source (mental, temporal and physical demand of the task, perceived effort, dissatisfaction for the performance and frustration due to the task). All personality traits except extraversion significantly interacted at least with one workload source. Perceived workload in flying column police officers appears to be the result of their personality characteristics interacting with the workload source. The implications of these results for the development of support measures aimed at reducing the impact of workload in this category of workers are discussed.
| S-EPMC4661321 | biostudies-literature
Project description:A lot of research has been done on the detection of mental workload (MWL) using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of measurement modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify MWL. The goal of this research was to classify perceived mental workload (PMWL) using a deep neural network (DNN) that flexibly makes use of multiple modalities, in order to allow for feature sharing between modalities. To achieve this goal, an experiment was conducted in which MWL was simulated with the help of verbal logic puzzles. The puzzles came in five levels of difficulty and were presented in a random order. Participants had 1 h to solve as many puzzles as they could. Between puzzles, they gave a difficulty rating between 1 and 7, seven being the highest difficulty. Galvanic skin response, photoplethysmograms, functional near-infrared spectrograms and eye movements were collected simultaneously using LabStreamingLayer (LSL). Marker information from the puzzles was also streamed on LSL. We designed and evaluated a novel intermediate fusion multimodal DNN for the classification of PMWL using the aforementioned four modalities. Two main criteria that guided the design and implementation of our DNN are modularity and generalisability. We were able to classify PMWL within-level accurate (0.985 levels) on a seven-level workload scale using the aforementioned modalities. The model architecture allows for easy addition and removal of modalities without major structural implications because of the modular nature of the design. Furthermore, we showed that our neural network performed better when using multiple modalities, as opposed to a single modality. The dataset and code used in this paper are openly available.
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Project description:BackgroundThere is strong evidence to show that lower nurse staffing levels in hospitals are associated with worse patient outcomes. One hypothesised mechanism is the omission of necessary nursing care caused by time pressure-'missed care'.AimTo examine the nature and prevalence of care left undone by nurses in English National Health Service hospitals and to assess whether the number of missed care episodes is associated with nurse staffing levels and nurse ratings of the quality of nursing care and patient safety environment.MethodsCross-sectional survey of 2917 registered nurses working in 401 general medical/surgical wards in 46 general acute National Health Service hospitals in England.ResultsMost nurses (86%) reported that one or more care activity had been left undone due to lack of time on their last shift. Most frequently left undone were: comforting or talking with patients (66%), educating patients (52%) and developing/updating nursing care plans (47%). The number of patients per registered nurse was significantly associated with the incidence of 'missed care' (p<0.001). A mean of 7.8 activities per shift were left undone on wards that are rated as 'failing' on patient safety, compared with 2.4 where patient safety was rated as 'excellent' (p < 0.001).ConclusionsNurses working in English hospitals report that care is frequently left undone. Care not being delivered may be the reason low nurse staffing levels adversely affects quality and safety. Hospitals could use a nurse-rated assessment of 'missed care' as an early warning measure to identify wards with inadequate nurse staffing.
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Project description:ContextTraditional methods of setting curricular guidelines using experts or consensus panels may miss important areas of knowledge, skills, and attitudes that need to be addressed in the training of medical students and residents.ObjectiveTo seek input from medical students and internal medicine residents ("trainees") on their perception of their needs for training in Geriatrics.DesignTwo assessment methods were used (1) focus groups with students and residents were conducted by professional facilitators and the transcripts analyzed for areas of agreement and divergence and (2) geriatric medicine experts and ward attendings were surveyed to examine training gaps raised by trainees during Geriatric Guest Attending Rounds.ResultsTrainees perceived training gaps in caring for elderly patients in the areas of (1) recognizing and addressing the complex, multifactorial nature of illness; (2) setting priorities and goals for work-up and intervention; (3) communication with families and with patients with cognitive disorders; (4) assessment of a patient for discharge from the hospital and the services at different sites in which patients may receive care. They recounted feeling overwhelmed by complex patients and social situations while acknowledging the special aspects of connecting with older patients. The gaps identified by trainees differ from and complement the curriculum guidelines set by expert recommendations.ConclusionTrainees identified gaps in skills and knowledge leading to trainee frustration and potentially adverse outcomes in caring for elderly patients. Development of curriculum guidelines should include assessment of trainees' perceived learning needs.
| S-EPMC1924752 | biostudies-literature
Project description:BackgroundDespite recent residency workload and hour limitations, little research on the relationship between workload and learning has been done. We sought to define residents' perceptions of the optimal patient workload for learning, and to determine how certain variables contribute to those perceptions. Our hypothesis was that the relationship between perceived workload and learning has a maximum point (forming a parabolic curve): that either too many or too few patients results in sub-optimal learning.MethodsResidents on inpatient services at two academic teaching hospitals reported their team and individual patient censuses, and rated their perception of their learning; the patient acuity; case variety; and how challenged they felt. To estimate maximum learning scores, linear regression models with quadratic terms were fit on learning score.ResultsResident self-perceived learning correlated with higher acuity and greater heterogeneity of case variety. The equation of census versus learning score, adjusted for perception of acuity and case mix scores, showed a parabolic curve in some cases but not in others.ConclusionThese data suggest that perceived resident workload is complex, and impacted by additional variables including patient acuity and heterogeneity of case variety. Parabolic curves exist for interns with regard to overall census and for senior residents with regard to new admissions on long call days.
| S-EPMC1550230 | biostudies-literature
Project description:ObjectiveTo establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses.Materials and methodsRepeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured work process data. Nurses rated their mental (ME) and physical exertion (PE) for each four-hour block, and recorded patient and staffing-level workload factors. Shift was the grouping variable in multilevel modeling where sensor-based measures were used to predict nursing perceptions of exertion.ResultsThere were 356 work hours from 89 four-hour shift segments across 35 bedside nursing shifts. In final models, sensor-based data accounted for 73% of between-shift, and 5% of within-shift variance in ME; and 55% of between-shift, and 55% of within-shift variance in PE. Significant predictors of ME were patient room noise (ß = 0.30, p < .01), the interaction between time spent and activity levels outside main work areas (ß = 2.24, p < .01), and the interaction between the number of patients on an insulin drip and the burstiness of speaking (ß = 0.19, p < .05). Significant predictors of PE were environmental service area noise (ß = 0.18, p < .05), and interactions between: entropy and burstiness of physical transitions (ß = 0.22, p < .01), time speaking outside main work areas and time at nursing stations (ß = 0.37, p < .001), service area noise and time walking in patient rooms (ß = -0.19, p < .05), and average patient load and nursing station speaking volume (ß = 0.30, p < .05).DiscussionAnalysis yielded highly predictive models of critical care nursing workload that generated insights into workflow and work design. Future work should focus on tighter connections to psychometric test development methods and expansion to a broader variety of settings and professional roles.ConclusionsSensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload.
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Project description:BACKGROUND:Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload. METHODS:An observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation. RESULTS:In the automated documentation time to documentation was reduced by 6.1?min (0.6?min vs 7.7?min, p?<? 0.05) and completeness increased (98% vs 95%, p?<? 0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p?<? 0.05). The same was true for frustration level (64 vs 33, p?<? 0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p?<?0.05). DISCUSSION:The study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system.
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Project description:BackgroundDespite the large body of evidence suggesting that effective public health infrastructure is vital to improving the health status of populations, many universities in developing countries offer minimal opportunities for graduate training in public health. In Nepal, for example, only two institutions currently offer a graduate public health degree. Both institutions confer only a general Masters in Public Health (MPH), and together produce 30 graduates per year. The objective of this assessment was to identify challenges in graduate public health education in Nepal, and explore ways to address these challenges.MethodsThe assessment included in-person school visits and data collection through semi-structured in-depth interviews with primary stakeholders of Nepal's public health academic sector. The 72 participants included faculty, students, alumni, and leaders of institutions that offered MPH programs, and the leadership of one government-funded institution that is currently developing an MPH program. Data were analyzed through content analysis to identify major themes.ResultsSix themes characterizing the challenges of expanding and improving graduate public health training were identified: 1) a shortage of trained public health faculty, with consequent reliance on the internet to compensate for inadequate teaching resources; 2) teaching/learning cultures and bureaucratic traditions that are not optimal for graduate education; 3) within-institution dominance of clinical medicine over public health; 4) a desire for practice-oriented, contextually relevant training opportunities; 5) a demand for degree options in public health specialties (for example, epidemiology); and 6) a strong interest in international academic collaboration.ConclusionDespite an enormous need for trained public health professionals, Nepal's educational institutions face barriers to developing effective graduate programs. Overcoming these barriers will require: 1) increasing the investment in public health education and 2) improving the academic environment of educational institutions. Long term, committed academic collaborations with international universities may be a realistic way to: 1) redress immediate inadequacies in resources, including teachers; 2) encourage learning environments that promote inquiry, creativity, problem-solving, and critical thinking; and 3) support development of the in-country capacity of local institutions to produce a cadre of competent, well-trained public health practitioners, researchers, teachers, and leaders.
| S-EPMC3640966 | biostudies-literature
Project description:Aims and objectivesTo clarify both the potential influencing factors and the current status of front-line nurses' work engagement, and thus provide a reference for targeted interventions.BackgroundAfter coronavirus disease 2019 outbreak, front-line nurses embraced remarkable potential stress and huge workload when caring for coronavirus disease 2019 patients, which may lead to new challenges to work engagement.DesignA large sample survey was conducted at the end of February 2020 in a designated hospital treating coronavirus disease 2019 patients in Wuhan, the capital of Hubei Province, in China. t Test, one-way ANOVA, chi-squared test, Pearson's correlation and hierarchical multiple regression were performed among 1,040 nurses using SPSS 24.0. The STROBE checklist was followed for observational studies.ResultsThe final model interpreted 27.3% of the variance, of which each block could explain 11.7%, 10.3% and 7.9% R2 changes including sociodemographic characteristics, stress and workload, respectively. Work engagement was negatively correlated with stress and workload. The potential influencing factors included sociodemographic characteristics (married, rescue staff, cabin ward), stress (infection control, PPE discomfort) and workload (mental demand, performance, frustration).ConclusionsFront-line nurses perceived low stress and workload, but high work engagement, especially in self-dedication. However, infection control, PPE discomfort and frustration were negatively associated with nurses' work engagement, while mental demand and good performance were positively associated with nurses' work engagement. Future interventions focused on decreasing front-line staff's infection risk and enhancing their self-confidence may be recommendable to promote their work engagement.
| S-EPMC8014711 | biostudies-literature