Table_1_Organizational commitment of emergency physician and its related factors: A national cross-sectional survey in China.DOCX

BackgroundOrganizational commitment is important for job performance and employee retention. However, studies on the level of organizational commitment and its related factors among emergency physicians in China are scarce. Therefore, this study aimed to identify the factors associated with organizational commitment among emergency physicians in China.MethodsA national cross-sectional study was conducted in 2018 among emergency physicians in China. Data were collected from 10,457 emergency physicians using a standard structured anonymous questionnaire, including demographic characteristics, organizational structure factors and work environment factors. A generalized linear model was used to explore the correlation between the independent variables and organizational commitment.ResultsIn this study, 55.3% of emergency physicians reported a moderate level of organizational commitment. The physicians who were male, younger than 40 years old, had a mid-level title and had a lower average monthly income were more likely to show lower organizational commitment levels. Conversely, the organizational commitment was higher among physicians who perceived that promotion is easy and the number of emergency physicians meet their daily work or had not experienced workplace violence in the last year.ConclusionsThe study showed that organizational commitment among Chinese emergency physicians was moderate and related to gender, age, monthly income, frequency of daily visits, departmental promotion mechanism and workplace violent. Targeted interventions are needed to improve the organizational commitment of emergency physicians in a comprehensive way.</p

Comparison of intraocular pressure by sex and birth cohorts in 2010 and 2012.

<p>Box plots showing the IOP of the right eye between the year 2010 and 2012 in each birth cohort of the population, ◇ represents a P value <0.05 calculated by paired t-test comparing the mean IOP of the year 2010 and 2012.</p

Effect of aging on intraocular pressure (IOP) in cross-sectional analysis.

<p>Cross-sectional change of IOP with increasing age for both genders. Each line was simply connected by 6 points which were the mean IOP value of the right eye of the six birth cohorts (50–54;55–59;60–64;65–69;70–74;75–79) from left to right, respectively. The error bars for each point show the upper and lower 95% confidence limits.</p

Baseline Characteristics of the Participants (mean ± standard deviation).

<p>Baseline Characteristics of the Participants (mean ± standard deviation).</p

Distribution of intraocular pressure among Chinese adults in Lingtou, China (2010).

<p>Histogram of Intraocular pressure for the population under study at baseline. Right eye data was used and the total number is 3372. The dark grey curve represents the normal distribution.</p

Visualization of the deep learning model on fundus images with different eye conditions.

The eye conditions from (a) to (g) are: (a) late dry age-related macular degeneration (AMD); (b) late wet AMD with disc tilt; (c) cataract; (d) glaucoma; (e) diabetic retinopathy; (f) high myopia; (g) vitreous opacity. (h) and (i) are two fundus images from both eyes of the same person. The bottom-right green text in each example indicates the corresponding probability scores outputted by the model (P1: left eye; P2: right eye).</p

Cross-sectional Associations of Related Risk Factors with IOP in 2010 according to Univariate and multivariate Regression Analyses.

<p>Cross-sectional Associations of Related Risk Factors with IOP in 2010 according to Univariate and multivariate Regression Analyses.</p

The real-time accuracy-loss curves of the 4 image preprocessing methods during 250 training epochs.

(a) Loss in the validation set; (b) Accuracy in the validation set.</p

The eye laterality distribution of the Yangxi and LabelMe Datasets.

The eye laterality distribution of the Yangxi and LabelMe Datasets.</p

Demonstration of the four image preprocessing methods.

(a) ORIGINAL; (b) CLAHE; (c) LSACR; (d) GRAY.</p