Search

Council members and policy workers need to understand (long-term) processes that lead to decisions. Gaining such an overview of a topic through a search engine can be challenging however, as searching a complex topic can result in an overwhelming number of documents and does not show how these documents are interrelated. This study investigates how to create an overview of a decision-making process, which may be integrated into a search engine. Interviews show that policy workers consider documents relevant to the overview when the document and proposal were both created in response to the same council decision document. We identify such provenance based on the co-citation of documents and textual references between documents. In an exploratory user study, policy workers are tasked to understand the development of policy proposals based on provided timelines. Their relevance assessments show that our approach nearly exclusively finds relevant documents (a precision of 0.97). Whereas the proposed approach identifies 91% of references made in documents, it only finds an exact target document in 39% of the total references. A further 52% of references finds a subset of documents including the target. A human in the loop can aid in finding the exact documents, and potentially add documents based on their domain expertise. The proposed approach creates an overview of a city council’s decision-making process on a given topic with high precision, and might apply to other domains oriented around a decision-making process.

Expert search systems help professionals find colleagues with specific expertise. Expert search results can be presented as a list of documents with their associated experts, or as a list of candidate experts with evidence for their expertise based on documents they authored. The type of result may affect search behaviour, and therefore search task performance. Previous work has not considered such effects from the result presentation, focusing instead on how to rank experts or on ways to interact with the search results. We compare the task performance of novice users using either a document-centric interface (where each search result is a document and its associated expert) or a candidate-centric interface (where each search result is a candidate expert and their associated documents). We also compare candidate-centric and document-centric ranking functions per interface. A post-experiment survey indicated that two variables affect which interface participants preferred: the retrieval unit (candidates or documents) and the complexity (number of documents per search result). These variables affected participants' search strategy, and consequently their task performance. A quantitative analysis revealed that 1) using the candidate-centric interface results in a higher rate of correctly completed tasks, as users evaluate candidates more thoroughly, and 2) the document-centric ranking yields faster task completion. Weak evidence of a statistical interaction effect was found that prevents a straightforward combination of the most effective interface type and the most efficient ranking type. Present work resulted in a more effective, albeit less efficient, search engine for expert search at the municipality of Utrecht.

On March 16, 2020, Prime Minister Rutte announced that the government was forced to switch from a strategy of local containment of outbreaks to a national strategy of maximum control. A week later, the first ‘intelligent’ national lockdown became a reality. In this contribution I will show that in the historical interaction between disease, health and social environment, connections are constantly made and severed between facts, opinions and expectations. As part of this historical process, the meanings attached to ‘infectious disease X’ are subject to change. I show how the Netherlands’ handling of the corona pandemic, and consequently the public health challenges, changed over time. Curfews and riots would erupt over the restrictions within the context of social amplification of conspiracy theories about the pandemic. At the same time there was a strong belief in a technological fix, with a magic bullet that would eliminate the virus. But Covid-19 turned out to be a fast-mutating virus that transformed continuously in response to human-environment interactions.

The new world was considered free of leprosy before the arrival of Europeans. In Suriname, historical migration routes suggest that leprosy could have been introduced from West Africa by the slave trade, from Asia by indentured workers, from Europe by the colonizers, and more recently by Brazilian gold miners. Previous molecular studies on environmental and ancient samples suggested a high variability of the strains circulating in the country, possibly resulting from the various migration waves. However, a current overview of such diversity in humans still needs to be explored. The origin and spread of leprosy in Suriname are investigated from a historical point of view and by strain genotyping of Mycobacterium leprae from skin biopsies of 26 patients with multibacillary leprosy using PCR-genotyping and whole-genome sequencing. Moreover, molecular signs of resistance to the commonly used anti-leprosy drugs i.e. dapsone, rifampicin and ofloxacin, were investigated. Molecular detection was positive for M. leprae in 25 out of 26 patient samples, while M. lepromatosis was not found in any of the samples. The predominant M. leprae strain in our sample set is genotype 4P (n=8) followed by genotype 1D-2 (n=3), 4N (n=2), and 4O/P (n=1). Genotypes 4P, 4N, 4O/P are predominant in West Africa and Brazil, and could have been introduced in Suriname by the slave trade from West Africa, and more recently by gold miners from Brazil. The presence of the Asian strains 1D-2 probably reflects an introduction by contract workers from India, China and Indonesia during the late 19th and early 20th century after the abolition of slavery. There is currently no definite evidence for the occurrence of the European strain 3 in the 26 patients. Geoplotting reflects internal migration, and also shows that most patients live in and around Paramaribo. A biopsy of one patient harbored two M. leprae genot

Background: Cannabis is one of the most popular drugs of the 21st century, especially among adolescents and young adults. Evidence of a variety of lasting neuropsychological deficits as a result of chronic cannabis use has increased. Furthermore, regular cannabis use is found to be a predictor of mental health problems, less motivation in school, and school dropout. Aim: Our goal is to propose a theoretical model of adolescent cannabis use disorder (CUD) based on Zinberg’s drug, set, and setting model and explicated by a review of the literature on adolescent cannabis use to improve the prevention and treatment of CUD for adolescents. Methods: PubMed and Web of Science were searched for relevant publications as part of a hypothesis-based and model-generating review. Results: Individual (set) and environmental (setting) risk factors play important roles in the development of CUD in adolescents. School performance, motivation, and attendance can be negatively influenced by persistent cannabis use patterns and adolescent brain development can consequently be impaired. Thus, cannabis use can be understood as both being the cause of poor school performance but also the consequence of poor school performance. To prevent and reduce adolescent CUD the drug, set, and setting must all be considered. It is important to notice that the multiple feedback loops (indicated in our dynamic interaction model) are not mutually exclusive, but offer important intervention focus points for social workers, addiction professionals, parents, and other care takers. Conclusion: We argue that the three dimensions of drug, set, and setting contribute significantly to the eventual manifestation of CUD. Based on our dynamic interaction model, recommendations are made for possible preventive and therapeutic interventions for the treatment of adolescents and young adults with CUD.

The ravaging COVID-19 pandemic has almost pushed into oblivion the fact that the United States is still struggling with an immense addiction crisis. Drug overdose deaths rose from 16,849 in 1999 to nearly 110,000—of which an estimated 75,000 involved opioids—in 2022. On a yearly basis, the opioid casualty rate is higher than the combined number of victims of firearm violence and car accidents. The COVID-19 epidemic might have helped to worsen the addiction crisis by stimulating drug use among adolescents and diverting national attention to yet another public health crisis. In the past decade, the sharpest increase in deaths occurred among those related to fentanyl and fentanyl analogs (illicitly manufactured, synthetic opioids of greater potency). In the first opioid crisis wave (1998–2010), opioid-related deaths were mainly associated with prescription opioids such as Oxycontin (oxycodone hydrochloride). The mass prescription of these narcotic drugs did anything but control the pervasive phenomenon of ‘addiction on prescription’ that played such an important role in the emergence and robustness of the US opioid crisis. Using a long-term drug lifecycle analytic approach, in this article I will show how opioid-producing pharmaceutical companies created a medical market for opioid painkillers. They thus fueled a consumer demand for potent opioid drugs that was eagerly capitalized on by criminal entrepreneurs and their international logistic networks. I will also point out the failure of US authorities to effectively respond to this crisis due to the gap between narcotic product regulation, regulation of marketing practices and the rise of a corporate-dominated health care system. Ironically, this turned the most powerful geopolitical force in the war against drugs into its greatest victim. Due to formulary availability and regulatory barriers to accessibility, European countries have been relatively protected against following suit the US opioid crisis.

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands.

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