Tuleohutusõpe haiglates

Jämedalt üldistades võib öelda, et pärast taasiseseisvumist Eestis tuleennetus1 mõneks ajaks peaaegu seiskus. Tule ohutus taandus teisejärguliseks, mööda vaadati ehituslikest tuleohutusnormidest, amortiseerusid tulekaitsepaigaldised2 ja unustusse vajusid regulaarsed tulekahjuõppused. Viimastel aastatel on jälle tuleennetusega tõsisemalt tegelema hakatud, ka Päästeamet tõstis 2006. a ennetustöö oma prioriteediks. Käesolev artikkel arutleb korraldusliku tulekaitse ühe võtmeküsimuse – tuleohutusõppe – üle, tuginedes viimaste aastate kogemusele Ida-Tallinna Keskhaiglas, Lääne-Tallinna Keskhaiglas ja Põhja-Eesti Regionaalhaiglas. Eesti Arst 2008; 87(4):310−31

EU wheat producer's area response to prices, climate and price risk

This thesis examines how changes in the wheat price, price risk and climate risk affect wheat producers’ area decisions in the EU. Numerous studies have estimated the wheat area response in the US and in developing countries, but few have focused on the EU. The attention to risk in previous studies has also been limited. Greatly influenced by the Common Agricultural Policy, it is likely that producers in the EU respond differently to prices and risk than producers in the rest of the world. Understanding how these factors affect area allocation in the EU is important to be able to predict global food supply, as the EU is one of the world’s main wheat producing regions. The study follows the Nerlovian partial adjustment approach and is conducted on panel data covering the period 2003 to 2022. The findings suggest that the wheat area response to wheat price is inelastic in both the short-run and the long-run. The estimated short-run elasticities vary from 0,09 to 0,15 and the estimated long-run elasticities vary from 0,57 to 0,82. The effect of climate risk on wheat area is negative, while the effect of price risk on wheat area is statistically insignificant. This suggests that risk mitigating measures in the EU are effective in terms of reducing price risk. However, with more frequent extreme weather events in the future due to climate change, different measures may be needed to reduce climate risk for wheat producers

Confirming the Validity of the School-Refusal Assessment Scale—Revised in a Sample of Children With Attention-Deficit/Hyperactivity Disorder

Children with developmental disorders, such as Attention-Deficit/Hyperactivity Disorder (ADHD), are at high risk of school-refusal behavior (SRB) compared with their peers. One of the most used scales to assess SRB is the School Refusal Behavior Scale – Revised (SRAS-R). The SRAS-R has demonstrated good psychometric properties when used with the general population of children but recently its validity has been questioned when used with children with developmental disorders. We tested the psychometric properties of the SRAS-R parental reports in 96 children with ADHD (Mage = 12.4; SD = 1.7, 61.5% boys). Results showed good model fit and internal consistency for the original four-factor structure. Three of the factors were strongly correlated, suggesting that SRB among children with ADHD are caused by several factors.publishedVersio

Addressing Non-Communicable Diseases in Fragile Lebanon: A Mixed-Methods Research Study

Introduction: Lebanon has faced a substantial increase in the burden of NonCommunicable Diseases (NCD) over the last decade. There is a dearth of research focusing on health systems and policy responses to NCD. This PhD thesis analyses how the NCD burden is addressed in the context of fragile Lebanon and identifies policy-, health system- and community-related factors affecting NCD prevention and control. Methodology: This thesis adopts a pragmatic paradigm and incorporates: 1. a political economy analysis of NCD, based on a literature review; 2. a system analysis of NCD prevention and control, based on semi-structured interviews and group-model building workshops with 79 health providers and community members in urban Greater Beirut; 3. a survey with 941 persons living with NCDs to identify the magnitude of key factors affecting NCD control in Greater Beirut. Findings: The political economy analysis revealed an unbalanced power relationship between NCD policy promoters (e.g. civil society) and blockers(e.g. private entities). This has led to a gap in the prevention policy landscape. Care is provided under the auspices of a highly privatized hospital-centric model where services are offered for commercial gain rather than public good. The systems analysis validated these insights, with health provider and community participants linking the challenging socio-political environment to lacking prevention policy/action. This increases NCD incidence and creates barriers in accessing care. Experiences with NCD care were noted to be varied and influenced by perceptions of service quality and trust in providers. The survey confirmed that inequities in access to care exist in Greater Beirut and highlighted that service delivery patterns differ by provider. Communities evaluated different dimensions of trust in healthcare and identified gaps in the reliability, fairness and fidelity of the current system. Conclusions: The thesis concludes with an overview of how to strengthen Lebanon’s response to NCDs

EMMA: Danish Natural-Language Processing of Emotion in Text: The new State-of-the-Art in Danish Sentiment Analysis and a Multidimensional Emotional Sentiment Validation Dataset

Sentiment analysis (SA) is the research and development field of computationally analysing emotion in text. One usage example of SA could be to track the sentiment of a company’s mentions on Twitter or to analyse a book’s positivity level. In this paper, we attempt to add to this work in two ways. First, we further develop the current tool Sentida (Lauridsen et al., 2019), which was originally developed to score valence in text. Valence is the amount of positivity in a text, e.g. a review. Our new version has a higher awareness of punctuation and syntax compared to the earlier version and shows significant improvement in classifying valence compared to the previous version in three different validation datasets (p < 0.01). Second, we develop a test dataset which future developers of SA can use called Emma (Emotional Multidimensional Analysis). In Emma, we supplement the dimension valence with a further three emotional dimensions: Intensity, dominance, and utility in a dataset of sentences scored by human coders on these four dimensions. The emotional dimensions are based on cognitive psychology work throughout the last 65 years. With Emma, we present both a more reliable validation dataset and the possibility of further improving the Danish SA field by using the dataset to train a neural network with machine learning for analysing more complex emotions in text. The current standard is the 1-dimensional classification of positivity in text, but with this approach, we allow for a classification in the four dimensions of the Emma dataset that reveals much more complex emotions in texts. To allow others to work with Sentida and Emma, we help update the currently available Sentida optimized for Python and publish Emma on Github.Sentiment analysis (SA) is the research and development field of computationally analysing emotion in text. One usage example of SA could be to track the sentiment of a company’s mentions on Twitter or to analyse a book’s positivity level. In this paper, we attempt to add to this work in two ways. First, we further develop the current tool Sentida (Lauridsen et al., 2019), which was originally developed to score valence in text. Valence is the amount of positivity in a text, e.g. a review. Our new version has a higher awareness of punctuation and syntax compared to the earlier version and shows significant improvement in classifying valence compared to the previous version in three different validation datasets (p < 0.01). Second, we develop a test dataset which future developers of SA can use called Emma (Emotional Multidimensional Analysis). In Emma, we supplement the dimension valence with a further three emotional dimensions: Intensity, dominance, and utility in a dataset of sentences scored by human coders on these four dimensions. The emotional dimensions are based on cognitive psychology work throughout the last 65 years. With Emma, we present both a more reliable validation dataset and the possibility of further improving the Danish SA field by using the dataset to train a neural network with machine learning for analysing more complex emotions in text. The current standard is the 1-dimensional classification of positivity in text, but with this approach, we allow for a classification in the four dimensions of the Emma dataset that reveals much more complex emotions in texts. To allow others to work with Sentida and Emma, we help update the currently available Sentida optimized for Python and publish Emma on Github