A model of teacher enthusiasm, teacher self-efficacy, grit, and teacher well-being among English as a foreign language teachers.

Introduction: This study aimed to investigate the relationship among teacher enthusiasm and teacher self-efficacy, grit, and teacher psychological well-being among Chinese English as a foreign language (EFL) teachers. Methods: A sample of 553 Chinese EFL teachers completed self-report measures of teacher enthusiasm, teacher self-efficacy, grit, and teacher psychological well-being. Confirmatory factor analysis was used to confirm the validity of the scales, and structural equation modeling was used to test the hypothesized model. Results: The results indicated that teacher self-efficacy and grit were positively associated with teacher psychological well-being, providing support for the importance of these teacher characteristics in promoting teacher well-being. Furthermore, teacher enthusiasm was found to have an indirect effect on teacher psychological well-being through the mediation of teacher grit, providing evidence for the importance of teacher motivation and engagement in promoting teacher well-being. The partial mediation model was found to be the best fitting model. Discussion: These findings have important implications for the development of interventions and programs aimed at promoting teacher well-being in the context of EFL teaching.

Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study.

BACKGROUND: Accurate detection is essential for brain metastasis (BM) management, but manual identification is laborious. This study developed, validated, and evaluated a BM detection (BMD) system. METHODS: Five hundred seventy-three consecutive patients (10 448 lesions) with newly diagnosed BMs and 377 patients without BMs were retrospectively enrolled to develop a multi-scale cascaded convolutional network using 3D-enhanced T1-weighted MR images. BMD was validated using a prospective validation set comprising an internal set (46 patients with 349 lesions; 44 patients without BMs) and three external sets (102 patients with 717 lesions; 108 patients without BMs). The lesion-based detection sensitivity and the number of false positives (FPs) per patient were analyzed. The detection sensitivity and reading time of three trainees and three experienced radiologists from three hospitals were evaluated using the validation set. RESULTS: The detection sensitivity and FPs were 95.8% and 0.39 in the test set, 96.0% and 0.27 in the internal validation set, and ranged from 88.9% to 95.5% and 0.29 to 0.66 in the external sets. The BMD system achieved higher detection sensitivity (93.2% [95% CI, 91.6-94.7%]) than all radiologists without BMD (ranging from 68.5% [95% CI, 65.7-71.3%] to 80.4% [95% CI, 78.0-82.8%], all P < .001). Radiologist detection sensitivity improved with BMD, reaching 92.7% to 95.0%. The mean reading time was reduced by 47% for trainees and 32% for experienced radiologists assisted by BMD relative to that without BMD. CONCLUSIONS: BMD enables accurate BM detection. Reading with BMD improves radiologists' detection sensitivity and reduces their reading times.

Occupational exposure to respirable dust from the coal-fired power generation process: sources, concentration, and health risk assessment.

To investigate the contamination levels of respirable dust released in the work environment and the induced workers' health risk at a coal-fired power plant, we collected 405 dust samples from different dusty workstations by personal sampling during the coal-fired power generation process. Then, an inhalation risk assessment model from the USEPA was combined with the Monte Carlo simulation method to quantitatively evaluate the health risk caused by dust inhalation. Of 10 workstations researched, the dust concentration in the most workstations exceeded the prescribed occupational exposure limit. Workers engaged in ash removal suffered the highest health risk at 4.08 × 10-6 ± 2.85 × 10-6 (95% CI), closely followed by those involved in other job categories. The results can contribute to the formulation of targeted dust prevention measures and implementation of risk management for the coal-fired power sector.

Evaluating Targeted Intervention on Coal Miners' Unsafe Behavior.

Miners' unsafe behavior is the main cause of roof accidents in coal mines, and behavior intervention plays a significant role in reducing the occurrence of miners' unsafe behavior. However, traditional behavior intervention methods lack pertinence. In order to improve the intervention effect and reduce the occurrence of coal mine roof accidents more effectively, this study proposed a targeted intervention method for unsafe behavior. The process of targeted intervention node locating was constructed, and based on the analysis of 331 coal mine roof accidents in China, three kinds of targeted intervention nodes were located. The effectiveness of targeted intervention nodes was evaluated by using structural equation model (SEM) through randomly distributing questionnaires to miners of Pingdingshan coal. The results show that, in preventing roof accidents of coal mines, the targeted intervention nodes have a significant positive impact on the intervention effect. The method can also be applied to the safety management of other industries by adjusting the node location and evaluation process.