Susan C. Hu

Job stress and burnout in hospital employees: comparisons of different medical professions in a regional hospital in Taiwan

Objectives To explore the prevalence and associated factors of burnout among five different medical professions in a regional teaching hospital. Design Cross-sectional study. Setting Hospital-based survey. Participants A total of 1329 medical professionals were recruited in a regional hospital with a response rate of 89%. These voluntary participants included 101 physicians, 68 physician assistants, 570 nurses, 216 medical technicians and 374 administrative staff. Primary and secondary outcome measures Demographic data included gender, age, level of education and marital status, and work situations, such as position, work hours and work shifts, were obtained from an electronic questionnaire. Job strain and burnout were measured by two validated questionnaires, the Chinese version of the Job Content Questionnaire and the Copenhagen Burnout Inventory. Results Among the five medical professions, the prevalence of high work-related burnout from highest to lowest was nurses (66%), physician assistants (61.8%), physicians (38.6%), administrative staff (36.1%) and medical technicians (31.9%), respectively. Hierarchical regression analysis indicated that job strain, overcommitment and low social support explained the most variance (32.6%) of burnout. Conclusions Physician assistant is an emerging high burnout group; its severity is similar to that of nurses and far more than that of physicians, administrative staff and medical technicians. These findings may contribute to the development of feasible strategies to reduce the stress which results in the burnout currently plaguing most hospitals in Taiwan.

Utilization of virtual samples to facilitate cancer identification for DNA microarray data in the early stages of an investigation

DNA microarray datasets are generally small in size, high dimensional with many non-discriminative genes, and non-linear with outliers. Their size/dimension ratio suggests that DNA microarray datasets are identified as small-sample problems. Recently, researchers have developed various gene selection algorithms to discover genes that are most relevant to a specific disease, and thus to reduce computation. Most gene selection algorithms improve learning performance and efficiency, but still suffer from the limitation of insufficient training samples in the datasets. Moreover, in the early stage of diagnosing a new disease, very limited data can be obtained. Therefore, the derived diagnostic model is usually unreliable to identify the new disease. Consequently, the diagnostic performance cannot always be robust, even with the gene selection algorithms. To solve the problem of very limited training dataset with non-linear data or outliers, we propose the method GVSG (Group Virtual Sample Generation), which is a non-linear Virtual Sample Generation algorithm. This non-linear method detects the characteristics in the very limited data, forms discrete groups of each discriminative gene, and systematically generates virtual samples for each of these to accelerate and stabilize the modeling process. The results show that this method significantly improves the learning accuracy in the early stage of DNA microarray data.

Depression Affects the Scores of All Facets of the WHOQOL-BREF and May Mediate the Effects of Physical Disability among Community-Dwelling Older Adults.

Background Geriatric depression is associated with the overall quality of life (QOL). However, how depressive symptoms affect the different domains and facets of QOL in older adults, and whether depressive symptoms mediate the relationship between physical disability and QOL in older adults are unclear. Methods A total of 490 ambulatory community-dwelling older adults aged 65 years or above were interviewed using the brief version of the World Health Organisation Quality of Life instrument (WHOQOL-BREF), the Modified Barthel Index (MBI), the 15-item Geriatric Depression Scale (GDS-15), and the Mini-Mental State Examination (MMSE). Sequential models for multiple linear regressions were analysed to determine if the MBI, GDS-15 and MMSE scores predict the WHOQOL-BREF scores. The potential mediation effects of depression (as determined by the GDS-15) on the relationship between MBI and WHOQOL-BREF were also analysed. Results The GDS-15 score was predictive of the scores of the four domains and all 26 facets of the WHOQOL-BREF. The significant predictive effects of the MBI score on 15 of the 26 facets of the WHOQOL-BREF were reduced to three after the adjustment for the GDS-15 score. Depression (as assessed by the GDS-15) is a mediator of the relationship between MBI and the physical, psychological and environmental domains of the WHOQOL-BREF. Conclusions Depression (assessed by the GDS-15) may affect the scores of every domain and all facets of the WHOQOL-BREF in the elderly. Furthermore, it may mediate the relationship between the MBI and on QOL scores. We recommend taking depressive symptoms into consideration when measuring community-dwelling older adults’ QOL and providing active ageing programs.

Acquiring knowledge with limited experience

Abstract: From computational learning theory, sample size in machine learning problems indeed affects the learning performance. Since only few samples can be obtained in the early stages of a system and fewer exemplars usually lead to a low learning accuracy, this research compares different machine learning methods through their classification accuracies to improve small-data-set learning. Techniques used in this paper include the mega-trend diffusion technique, a backpropagation neural network, a support vector machine, and decision trees to explore the machine learning issue with two real medical data sets concerning cancer. The result of the experiment shows that the mega-trend diffusion technique and backpropagation approaches are effective methods of small-data-set learning.

A Population-Based Study of All-Cause Mortality and Cardiovascular Disease in Association With Prior History of Hypoglycemia Among Patients With Type 1 Diabetes

OBJECTIVE This study investigated the effects of severe hypoglycemia on risks of all-cause mortality and cardiovascular disease (CVD) incidence in patients with type 1 diabetes mellitus (T1DM). RESEARCH DESIGN AND METHODS Two nested case-control studies with age- and sex-matched control subjects and using the time-density sampling method were performed separately within a cohort of 10,411 patients with T1DM in Taiwan. The study enrolled 564 nonsurvivors and 1,615 control subjects as well as 743 CVD case subjects and 1,439 control subjects between 1997 and 2011. History of severe hypoglycemia was identified during 1 year, 1–3 years, and 3–5 years before the occurrence of the study outcomes. Conditional logistic regression analyses were performed to estimate the odds ratio (OR) and 95% CI of the study outcomes. RESULTS Prior severe hypoglycemic events within 1 year were associated with higher risks of all-cause mortality and CVD (adjusted OR 2.74 [95% CI 1.96–3.85] and 2.02 [1.35–3.01], respectively). Events occurring within 1–3 years and 3–5 years before death were also associated with adjusted ORs of 1.94 (95% CI 1.39–2.71) and 1.68 (1.15–2.44), respectively. Significant dose–gradient effects of severe hypoglycemia frequency on mortality and CVD were observed within 5 years. CONCLUSIONS Although the CVD incidence may be associated with severe hypoglycemic events occurring in the previous year, the risk of all-cause mortality was associated with severe hypoglycemic events occurring in the preceding 5 years. Exposure to repeated severe hypoglycemic events can lead to higher risks of mortality and CVD.

Effects of different amounts of exercise on preventing depressive symptoms in community-dwelling older adults: a prospective cohort study in Taiwan

Objectives To compare the effects of four different amounts of exercise for preventing depressive symptoms in community-dwelling older adults. Design Prospective cohort study. Setting A nationally representative sample in Taiwan. Participants Four waves of the survey ‘Taiwan Longitudinal Study on Aging (TLSA)’ from 1996 to 2007 were analysed. A total of 2673 older adults aged 65 years and over were recruited. Primary and secondary outcome measures Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale (CESD). Four different types/amounts of exercise were examined including: (1) 3 times/week, 15 min/time; (2) 3 times/week, 30 min/time; (3) 6 times/week, 15 min/time; and (4) 6 times/week, 30 min/time. All exercise types were required to have at least moderate intensity. The impacts of different amounts of exercise on depressive symptoms were analysed using generalised linear mixed models. Results More than one-fifth of the elder individuals under consideration had depressive symptoms (CESD ≥10). About 38.6% of older adults met the lowest criteria for exercise type 1, and fewer (28.0%) met the highest criteria for type 4. Only exercise type 4 in the current survey was initially related to lower depressive symptoms (OR=0.8, 95% CI 0.66 to 0.95). However, after considering the interaction between time and changes in exercise patterns, the results showed that all persistent exercise models, even if a very low amount (3 times/week, 15 min/time), had significantly preventive effects on depressive symptoms (OR=0.56~0.67). Conclusion Consistent exercise with at least 15 min per time, three times a week of moderate intensity is significantly associated with lower risk of depressive symptoms. This low amount of exercise may be easier to promote at the community and population level than other alternatives. Trial registration Registry number 104040 of the Institutional Ethics Committee of Chia-Yi Christian Hospital.

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets

It is difficult for learning models to achieve high classification performances with imbalanced data sets, because with imbalanced data sets, when one of the classes is much larger than the others, most machine learning and data mining classifiers are overly influenced by the larger classes and ignore the smaller ones. As a result, the classification algorithms often have poor learning performances due to slow convergence in the smaller classes. To balance such data sets, this paper presents a strategy that involves reducing the sizes of the majority data and generating synthetic samples for the minority data. In the reducing operation, we use the box-and-whisker plot approach to exclude outliers and the Mega-Trend-Diffusion method to find representative data from the majority data. To generate the synthetic samples, we propose a counterintuitive hypothesis to find the distributed shape of the minority data, and then produce samples according to this distribution. Four real datasets were used to examine the performance of the proposed approach. We used paired t-tests to compare the Accuracy, G-mean, and F-measure scores of the proposed data pre-processing (PPDP) method merging in the D3C method (PPDP+D3C) with those of the one-sided selection (OSS), the well-known SMOTEBoost (SB) study, and the normal distribution-based oversampling (NDO) approach, and the proposed data pre-processing (PPDP) method. The results indicate that the classification performance of the proposed approach is better than that of above-mentioned methods.

Work-Related Psychosocial Hazards and Arteriosclerosis:A Cross-Sectional Study Among Medical Employees in a Regional Hospital in Taiwan

The association of psychosocial stress with cardiovascular disease (CVD) is still inconclusive. The aim of this study was to examine the relationships between arteriosclerosis and various work-related conditions among medical employees with various job titles.A total of 576 medical employees of a regional hospital in Taiwan with a mean age of 43 years and female gender dominance (85%) were enrolled. Arteriosclerosis was evaluated by brachial-ankle pulse wave velocity (baPWV). Workrelated conditions included job demands, job control, social support, shift work, work hours, sleep duration, and mental health. The crude relationship between each of the selected covariates and baPWV was indicated by Spearman correlation coefficients. A multiple linear regression model was further employed to estimate the adjusted associations of selected covariates with arteriosclerosis.The mean baPWV of participants was 11.4 ± 2.2 m/s, with the value for males being significantly higher than that for females. The baPWV was associated with gender, age, medical profession, work hours, work type, depression, body mass index, systolic and diastolic blood pressures, fasting glucose, and cholesterol. After being fully adjusted by these factors, only sleep duration of less than 6 hours and weekly work hours longer than 60 hours were significantly associated with increased risk of arteriosclerosis. The conditions of job demands, job control, social support, shift work, and depression showed no significant association with baPWV.Longer work hours and shorter sleep durations were associated with an increased risk of arteriosclerosis. These findings should make it easier for the employer or government to stipulate rational work hours in order to avoid the development of cardiovascular disease among their employees.

Aerobic-synergized exercises may improve fall-related physical fitness in older adults

BACKGROUND The purpose of the present study was to determine whether a synergistic exercise model based on aerobics with additional fall-preventive components could provide extra benefits compared with the same duration of aerobic-synergistic exercise alone. METHODS A total of 102 adults aged 65 years and over from three geographically separated communities were assigned to three groups: the general aerobic exercise (GAE) group (N.=44), the GAE plus ball game group (BG group; N.=30) and the GAE plus square-stepping exercise group (SSE group; N.=28). Each group participated in one hour of exercise intervention and two hours of leisure activities twice weekly for 12 weeks. Each exercise session consisted of one hour of combined exercises performed in the following order: 10 minutes of warm-up activities, 20 minutes of aerobics, 20 minutes of the respective exercise model, and 10 minutes of cool-down activities. Functional fitness tests, including aerobic endurance, leg strength, flexibility, reaction time, static balance and mobility, were measured before and after the intervention. Paired t-tests and mixed model analyses were conducted to compare the differences in each measurement within and among the groups. RESULTS All of the groups exhibited significantly positive effects (P<0.05), including improvements in aerobic endurance, leg muscle strength, static balance, and mobility, after the intervention. There were no significant differences in these improvements in the other two groups compared with group GAE. However, group BG and group SSE showed significantly greater improvements in mobility compared with group GAE (P<0.05). CONCLUSIONS We conclude that a combination of aerobics and selected fall-prevention exercises performed over a consistent period may improve mobility without compromising the fundamental benefits of aerobics. Future studies using randomized control trials with recorded fall events and a longer period of follow-up are indicated to validate the effects of fall prevention exercises.

The Relationship between Urbanization, the Built Environment, and Physical Activity among Older Adults in Taiwan

Urbanization and ageing are global phenomena and offer unique challenges in different countries. A supportive environment plays a critical role in addressing the issue of behavioral change and health promotion among older adults. Many studies in the U.S., EU, and Australia have considered promoting physical activity in the community based on ecological models, whereas very few Asian studies have examined the relationships among urbanization, the built environment and physical activity in elderly at the ecological level, especially from a multi-level perspective. Due to the prevalence of post-war baby boomers and a very low birth-rate, the older population (aged 65 years old and older) in Taiwan has increased rapidly since 2011 and has exceeded the younger generation (0–14 years old) in 2017. Hence, the purpose of this study was first to examine the degree of urbanization in townships and the status of related built environments in Taiwan and then to investigate whether the built environment is associated with recommended amounts of physical activity among older adults. Three national datasets and a multi-level design were used in this research. Data at the individual level was obtained from the 2009 National Health Interview Survey (NHIS) which was taken from June 2009 to February 2010. Ecological data was obtained from the 2006 National Land Use Investigation of the National Geographic Information System and the 2010 Population and Housing Census. The analyses included a descriptive analysis, a bivariate analysis, a multiple logistic regression, and a multi-level analysis, utilizing a mostly hierarchical linear model (HLM). The results showed a significant relationship between factors at the environmental levels and physical activity in older adults. Urbanization, the built environment, and the median income of townships were positively correlated to the physical activity of the older adults. After controlling for individual-level factors, urbanization still exhibited this correlation. Parks and green spaces were associated with achieving the recommended amount of physical activity. However, there was no relationship after controlling for factors at the individual level. Detailed discussions were provided.