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Wanneer verschijnt de eerste timmerman?

Physiotherapy students' perceived stress, stressors, and reactions to stressors

BACKGROUND AND PURPOSE: Studies of healthcare students report increased levels of stress, with academic pressures being the greatest source. The objective of this study was to examine the differences in the overall stress level, stressors, and reactions to stressors between physiotherapy students at the University of Gothenburg (GU) and those at the Hanze University of Applied Sciences (HUAS).METHODS: The Student-life Stress Inventory was used. The participants (n = 116) included physiotherapy students at GU and HUAS. The distribution of the questionnaire occurred during a regular lecture or in a lecture that was scheduled particularly for its distribution.RESULTS: At GU, 13.7% of the students rated their level of stress as mild, whereas 72.5% of them rated it as moderate. The corresponding values for HUAS students were 43.9% and 43.9%, respectively. This difference between two universities was significant (p = 0.006). The total score of the subcategories indicated that the students at GU reported significantly higher levels of stressors (p = 0.027) and reactions to stressors (p = 0.003). However, there were no significant differences in the male participants between the universities.CONCLUSION: Female students in their three-year educational program in Sweden experienced significantly more stress than Dutch female students in their four-year educational program.

Physiotherapy students' perceived stress, stressors, and reactions to stressors

BACKGROUND AND PURPOSE: Studies of healthcare students report increased levels of stress, with academic pressures being the greatest source. The objective of this study was to examine the differences in the overall stress level, stressors, and reactions to stressors between physiotherapy students at the University of Gothenburg (GU) and those at the Hanze University of Applied Sciences (HUAS).METHODS: The Student-life Stress Inventory was used. The participants (n = 116) included physiotherapy students at GU and HUAS. The distribution of the questionnaire occurred during a regular lecture or in a lecture that was scheduled particularly for its distribution.RESULTS: At GU, 13.7% of the students rated their level of stress as mild, whereas 72.5% of them rated it as moderate. The corresponding values for HUAS students were 43.9% and 43.9%, respectively. This difference between two universities was significant (p = 0.006). The total score of the subcategories indicated that the students at GU reported significantly higher levels of stressors (p = 0.027) and reactions to stressors (p = 0.003). However, there were no significant differences in the male participants between the universities.CONCLUSION: Female students in their three-year educational program in Sweden experienced significantly more stress than Dutch female students in their four-year educational program.

The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

The application of natural language processing for the extraction of mechanistic information in toxicology

To study the ways in which compounds can induce adverse effects, toxicologists have been constructing Adverse Outcome Pathways (AOPs). An AOP can be considered as a pragmatic tool to capture and visualize mechanisms underlying different types of toxicity inflicted by any kind of stressor, and describes the interactions between key entities that lead to the adverse outcome on multiple biological levels of organization. The construction or optimization of an AOP is a labor intensive process, which currently depends on the manual search, collection, reviewing and synthesis of available scientific literature. This process could however be largely facilitated using Natural Language Processing (NLP) to extract information contained in scientific literature in a systematic, objective, and rapid manner that would lead to greater accuracy and reproducibility. This would support researchers to invest their expertise in the substantive assessment of the AOPs by replacing the time spent on evidence gathering by a critical review of the data extracted by NLP. As case examples, we selected two frequent adversities observed in the liver: namely, cholestasis and steatosis denoting accumulation of bile and lipid, respectively. We used deep learning language models to recognize entities of interest in text and establish causal relationships between them. We demonstrate how an NLP pipeline combining Named Entity Recognition and a simple rules-based relationship extraction model helps screen compounds related to liver adversities in the literature, but also extract mechanistic information for how such adversities develop, from the molecular to the organismal level. Finally, we provide some perspectives opened by the recent progress in Large Language Models and how these could be used in the future. We propose this work brings two main contributions: 1) a proof-of-concept that NLP can support the extraction of information from text for modern toxicology and 2) a template open-source model for recognition of toxicological entities and extraction of their relationships. All resources are openly accessible via GitHub (https://github.com/ontox-project/en-tox).

Exploring experiences with stressors and coping resources among Dutch socioeconomic groups during the COVID-19 pandemic

The COVID-19 crisis impacts populations globally. This impact seems to differ for groups with low- and high-socioeconomic status (SES). We conducted a qualitative study in the Netherlands using a salutogenic perspective to examine experiences with stressors and coping resources during the pandemic among both SES groups to gain insight on how to promote the health and well-being of these groups. We conducted 10 focus group discussions and 20 interviews to explore the experiences, including resources and stressors, of respondents from low- (N = 37) and high-SES (N = 38) groups (25-55 years, Dutch speaking). We analyzed the findings at individual, community, and national levels. The results show that coping depends on government-imposed measures and the way individuals handle these measures; restriction to the home context with positive and negative consequences for work and leisure; psychological negative consequences and resourcefulness; and social effects related to unity (e.g. social cohesion or support) and division (including polarization). Respondents with lower SES expressed more problems with COVID-19 measures and experienced more social impact in their neighborhood than those with higher SES. Where low-SES groups especially mentioned the effects of staying at home on family life, high-SES groups mentioned effects on work life. At last, psychological consequences seem to differ somewhat across SES groups. Recommendations include consistent government-imposed measures and government communication, support for home schooling children, and strengthening the social fabric of neighborhoods.