Prediction of well-being and insight into work-life integration among physicians using machine learning approach.
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ABSTRACT:
SUBMITTER: Nishi M
PROVIDER: S-EPMC8282024 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ImportancePoor work-life integration (WLI) occurs when career and personal responsibilities come in conflict and may contribute to the ongoing high rates of physician burnout. The characteristics associated with WLI are poorly understood.ObjectiveTo identify personal and professional factors associated with WLI in physicians and identify factors that modify the association between gender and WLI.Design, setting, and participantsThis cross-sectional study was based on electronic and paper surveys administered October 2017 to March 2018 at private, academic, military, and veteran's practices across the US. It used a population-based sample of US physicians across all medical specialties. Data analysis was performed from November 2019 to July 2020.Main outcomes and measuresWLI was assessed using an 8-item scale (0-100 point scale, with higher scores indicating favorable WLI), alongside personal and professional factors. Multivariable linear regressions evaluated independent associations with WLI as well as factors that modify the association between gender and WLI.ResultsOf 5197 physicians completing surveys, 4370 provided complete responses. Of the physicians who provided complete responses, 2719 were men, 3491 were White/Caucasian (80.8%), 3560 were married (82.4%), and the mean (SD) age was 52.3 (12.0) years. The mean (SD) WLI score was 55 (23). Women reported lower (worse) mean (SD) WLI scores than men overall (52 [22] vs 57 [23]; mean difference, -5 [-0.2 SDs]; P < .001). In multivariable regression, lower WLI was independently associated with being a woman (linear regression coefficient, -6; SE, 0.7; P < .001) as well as being aged 35 years or older (eg, aged 35 to 44 years: linear regression coefficient, -7; SE, 1.4; P < .001), single (linear regression coefficient, -3 vs married; SE, 1.1; P = .003), working more hours (eg, 50 to 59 hours per week vs less than 40 hours per week: linear regression coefficient, -9; SE, 1.0; P < .001) and call nights (linear regression coefficient, -1 for each call night per week; SE, 0.2; P < .001), and being in emergency medicine (linear regression coefficient, -18; SE, 1.6, P < .001), urology (linear regression coefficient, -11; SE, 4.0; P = .009), general surgery (linear regression coefficient, -4; SE, 2.0; P = .04), anesthesiology (linear regression coefficient, -4; SE, 1.7; P = .03), or family medicine (linear regression coefficient, -3; SE, 1.4; P = .04) (reference category, internal medicine subspecialties). In interaction modeling, physician age, youngest child's age, and hours worked per week modified the associations between gender and WLI, such that the largest gender disparities were observed in physicians who were aged 45 to 54 years (estimated WLI score for women, 49; 95% CI, 47-51; estimated WLI score for men, 57, 95% CI, 55-59; P < .001), had youngest child aged 23 years or older (estimated WLI score for women, 51; 95% CI, 48-54; estimated WLI score for men, 60; 95% CI, 58-62; P < .001), and were working less than 40 hours per week (estimated WLI score for women, 61; 95% CI, 59-63; estimated WLI score for men; 70; 95% CI, 68-72; P < .001).Conclusions and relevanceThis study found that lower WLI was reported by physicians who are women, single, aged 35 years or older, and who work more hours and call nights. These findings suggest that systemic change is needed to improve WLI among physicians.
| S-EPMC8160595 | biostudies-literature
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:IntroductionIntegrating work and home domains is a constant challenge for medical professionals. Only half of physicians report positive work-life satisfaction, implying that negotiating the inherent conflicts between work and home may not be intuitive. Early teaching of skills focused on professional sustainability may best prepare physicians to navigate conflicts between work and home domains.MethodsThis interactive workshop targets trainees and junior faculty. It aims to highlight the current state of physician career satisfaction, to bring awareness to the risk of physician burnout, and to apply strategies that promote work-life integration as a lifelong practice for sustained career satisfaction. It includes a detailed presentation with structured resources to reinforce skill development.ResultsThis workshop was delivered five times to trainees and junior faculty. Workshop evaluations (n = 50) revealed that all participants believed the information presented was useful, addressed competencies relevant to their training, and increased their knowledge about how to create better work-life integration; all anticipated improvement in their professional work. They all recommended this program to a colleague.DiscussionThis workshop offers an effective way to teach a skill set that enhances physicians' abilities to negotiate conflicting work and life domain boundaries. Our results indicate that learners intend to apply newly acquired strategies for work-life integration so as to improve career satisfaction and wellness. Such skill sets may mitigate physician burnout and promote career sustainability, both critical issues with far-reaching implications for the delivery of safe, high-quality health care at the provider and system levels.
| S-EPMC6374746 | biostudies-literature
Project description:In response to the outbreak of the COVID-19 pandemic, educational institutions around the world were forced into lockdown in order to contain the spread of the virus. To ensure continuous provision of education, most transitioned to emergency remote instruction. This has been particularly the case in higher education (HE) institutions. The circumstances of the pandemic have brought unprecedented psychological pressure on the population, in the case of educators and students exacerbated by the transition to a mode of instruction that was completely novel to the majority. The present study examines how college and university instructors dealt with teaching online in these unparalleled circumstances, with a focus on how factors connected with their daily lives and livelihoods influenced their well-being. Between April and September 2020, a comprehensive online survey was filled out by 804 HE instructors from 92 countries. We explore how sociodemographic variables such as gender, age, relationship status, living conditions, and length of professional experience non-trivially affect situational anxiety, work-life synergy, coping, and productivity. The results contribute to a better understanding of the impact of the pandemic and emergency remote instruction on college and university instructors' well-being by explaining the mechanisms mediating the relationship between individual, contextual, and affective variables. It may provide helpful guidelines for college and university administrators as well as teachers themselves as to how help alleviate the adverse effects of the continuing pandemic and possible similar disruptions leading to school closures on coping and well-being.
| S-EPMC8226323 | biostudies-literature
Project description:Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.
| S-EPMC8563624 | biostudies-literature
Project description:CRISPR interference (CRISPRi), the targeting of a catalytically dead Cas protein to block transcription, is the leading technique to silence gene expression in bacteria. However, design rules for CRISPRi remain poorly defined, limiting predictable design for gene interrogation, pathway manipulation, and high-throughput screens. Here we develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in multiple genome-wide essentiality screens, with the surprising discovery that gene-specific features such as transcriptional activity substantially impact prediction of guide activity. Accounting for these features as part of algorithm development allowed us to develop a mixed-effect random forest regression model that provides better estimates of guide efficiency than existing methods, as demonstrated in an independent saturating screen. We further applied methods from explainable AI to extract interpretable design rules from the model, such as sequence preferences in the vicinity of the PAM distinct from those previously described for genome engineering applications. Our approach provides a blueprint for the development of predictive models for CRISPR technologies where only indirect measurements of guide activity are available.
2023-12-19 | GSE196911 | GEO
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status.
2013-01-01 | GSE29210 | GEO
Project description:Given that millions of employees switched to mandatory telework during COVID-19, and as teleworking practices are likely to continue, it is essential to understand the potential impact of mandatory and non-flexible teleworking practices on employee well-being. Drawing on Conservation of Resources theory, we find support for resource gains as increase in job control mediates the link between increases in teleworking and improvements in well-being (increases in work engagement and decreases in burnout and job boredom). Conversely, the findings indicate resource losses as loss of social support and higher work-non-work interference explain the deterioration in well-being for those whose teleworking increased since the COVID-19 outbreak. By coupling role depletion and role enrichment theories, we find that employees who had children living at home experienced greater work-non-work interference due to increased teleworking. However, having children also buffered the negative impact of work-non-work interference on well-being. The findings are based on a Finnish population sample collected three months before and after the COVID-19 outbreak in March 2020 (N = 996 of matched respondents) and latent change score analyses of within-person changes. Our results provide new insights regarding the potential impact of teleworking on employee well-being in a context where teleworking is not flexible and self-selected by the employees. Notably, our results draw bridges (rather than burn them) between role depletion and enrichment frameworks as having children may simultaneously be “a burden” and “a gift”. Supplementary information The online version contains supplementary material available at 10.1007/s12144-023-04250-8.
| S-EPMC9878489 | biostudies-literature
Project description:BackgroundMalonylation is a recently discovered post-translational modification that is associated with a variety of diseases such as Type 2 Diabetes Mellitus and different types of cancers. Compared with experimental identification of malonylation sites, computational method is a time-effective process with comparatively low costs.ResultsIn this study, we proposed a novel computational model called Mal-Prec (Malonylation Prediction) for malonylation site prediction through the combination of Principal Component Analysis and Support Vector Machine. One-hot encoding, physio-chemical properties, and composition of k-spaced acid pairs were initially performed to extract sequence features. PCA was then applied to select optimal feature subsets while SVM was adopted to predict malonylation sites. Five-fold cross-validation results showed that Mal-Prec can achieve better prediction performance compared with other approaches. AUC (area under the receiver operating characteristic curves) analysis achieved 96.47 and 90.72% on 5-fold cross-validation of independent data sets, respectively.ConclusionMal-Prec is a computationally reliable method for identifying malonylation sites in protein sequences. It outperforms existing prediction tools and can serve as a useful tool for identifying and discovering novel malonylation sites in human proteins. Mal-Prec is coded in MATLAB and is publicly available at https://github.com/flyinsky6/Mal-Prec , together with the data sets used in this study.
| S-EPMC7682087 | biostudies-literature
Project description:This article investigates a change in the structuring of work time, using a natural experiment to test whether participation in a corporate initiative (Results Only Work Environment; ROWE) predicts corresponding changes in health-related outcomes. Drawing on job strain and stress process models, we theorize greater schedule control and reduced work-family conflict as key mechanisms linking this initiative with health outcomes. Longitudinal survey data from 659 employees at a corporate headquarters shows that ROWE predicts changes in health-related behaviors, including almost an extra hour of sleep on work nights. Increasing employees' schedule control and reducing their work-family conflict are key mechanisms linking the ROWE innovation with changes in employees' health behaviors; they also predict changes in well-being measures, providing indirect links between ROWE and well-being. This study demonstrates that organizational changes in the structuring of time can promote employee wellness, particularly in terms of prevention behaviors.
| S-EPMC3267478 | biostudies-literature