Asthma referrals : a key component of asthma management that needs to be addressed

Peer reviewedPublisher PD

Does clinical management improve outcomes following self-Harm? Results from the multicentre study of self-harm in England

Background Evidence to guide clinical management of self-harm is sparse, trials have recruited selected samples, and psychological treatments that are suggested in guidelines may not be available in routine practice. Aims To examine how the management that patients receive in hospital relates to subsequent outcome. Methods We identified episodes of self-harm presenting to three UK centres (Derby, Manchester, Oxford) over a 10 year period (2000 to 2009). We used established data collection systems to investigate the relationship between four aspects of management (psychosocial assessment, medical admission, psychiatric admission, referral for specialist mental health follow up) and repetition of self-harm within 12 months, adjusted for differences in baseline demographic and clinical characteristics. Results 35,938 individuals presented with self-harm during the study period. In two of the three centres, receiving a psychosocial assessment was associated with a 40% lower risk of repetition, Hazard Ratios (95% CIs): Centre A 0.99 (0.90–1.09); Centre B 0.59 (0.48–0.74); Centre C 0.59 (0.52–0.68). There was little indication that the apparent protective effects were mediated through referral and follow up arrangements. The association between psychosocial assessment and a reduced risk of repetition appeared to be least evident in those from the most deprived areas. Conclusion These findings add to the growing body of evidence that thorough assessment is central to the management of self-harm, but further work is needed to elucidate the possible mechanisms and explore the effects in different clinical subgroups

Unraveling the Relation Between Reading Comprehension and Print Exposure

The purpose of this study was to test the directionality of influence between reading comprehension (RC) and print exposure (PE), thereby estimating genetic and environmental effects of this relation. The sample consisted of 910 twins in fourth through ninth grades (Mage = 12.33 years, SD = 1.41) from the Florida Twin Project on Reading, Behavior, and Environment. Using direction-of-causation model in a twin design, results supported a direction of influence running from RC to PE. This relation was underpinned by genetic and environmental factors of RC as well as PE. Implications for reading education are discussed

Chlamydia control activities in Europe: cross-sectional survey

Background: Chlamydia is the most commonly reported bacterial sexually transmitted infection in Europe. The objective of the Screening for Chlamydia in Europe (SCREen) project was to describe current and planned chlamydia control activities in Europe. Methods: The authors sent a questionnaire asking about different aspects of chlamydia epidemiology and control to public health and clinical experts in each country in 2007. The principles of sexually transmitted infection control were used to develop a typology comprising five categories of chlamydia control activities. Each country was assigned to a category, based on responses to the questionnaire. Results: Experts in 29 of 33 (88%) invited countries responded. Thirteen of 29 countries (45%) had no current chlamydia control activities. Six countries in this group stated that there were plans to introduce chlamydia screening programmes. There were five countries (17%) with case management guidelines only. Three countries (10%) also recommended case finding amongst partners of diagnosed chlamydia cases or people with another sexually transmitted infection. Six countries (21%) further specified groups of asymptomatic people eligible for opportunistic chlamydia testing. Two countries (7%) reported a chlamydia screening programme. There was no consistent association between the per capita gross domestic product of a country and the intensity of chlamydia control activities (P = 0.816). Conclusion: A newly developed classification system allowed the breadth of ongoing national chlamydia control activities to be described and categorized. Chlamydia control strategies should ensure that clinical guidelines to optimize chlamydia diagnosis and case management have been implemented before considering the appropriateness of screening programmes

Can Peanuts Fall in Love with Distributional Semantics?

The context in which a sentence appears can drastically alter our expectations about upcoming words - for example, following a short story involving an anthropomorphic peanut, experimental participants are more likely to expect the sentence 'the peanut was in love' than 'the peanut was salted', as indexed by N400 amplitude (Nieuwland & van Berkum, 2006). This rapid and dynamic updating of comprehenders' expectations about the kind of events that a peanut may take part in based on context has been explained using the construct of Situation Models - updated mental representations of key elements of an event under discussion, in this case, the peanut protagonist. However, recent work showing that N400 amplitude can be predicted based on distributional information alone raises the question whether situation models are in fact necessary for the kinds of contextual effects observed in previous work. To investigate this question, we attempt to model the results of Nieuwland and van Berkum (2006) using six computational language models and three sets of word vectors, none of which have explicit situation models or semantic grounding. We find that the effect found by Nieuwland and van Berkum (2006) can be fully modeled by two language models and two sets of word vectors, with others showing a reduced effect. Thus, at least some processing effects normally explained through situation models may not in fact require explicit situation models

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

Abstract grammatical knowledge - of parts of speech and grammatical patterns - is key to the capacity for linguistic generalization in humans. But how abstract is grammatical knowledge in large language models? In the human literature, compelling evidence for grammatical abstraction comes from structural priming. A sentence that shares the same grammatical structure as a preceding sentence is processed and produced more readily. Because confounds exist when using stimuli in a single language, evidence of abstraction is even more compelling from crosslingual structural priming, where use of a syntactic structure in one language primes an analogous structure in another language. We measure crosslingual structural priming in large language models, comparing model behavior to human experimental results from eight crosslingual experiments covering six languages, and four monolingual structural priming experiments in three non-English languages. We find evidence for abstract monolingual and crosslingual grammatical representations in the models that function similarly to those found in humans. These results demonstrate that grammatical representations in multilingual language models are not only similar across languages, but they can causally influence text produced in different languages.Comment: Accepted at EMNLP 202

When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages

Multilingual language models are widely used to extend NLP systems to low-resource languages. However, concrete evidence for the effects of multilinguality on language modeling performance in individual languages remains scarce. Here, we pre-train over 10,000 monolingual and multilingual language models for over 250 languages, including multiple language families that are under-studied in NLP. We assess how language modeling performance in each language varies as a function of (1) monolingual dataset size, (2) added multilingual dataset size, (3) linguistic similarity of the added languages, and (4) model size (up to 45M parameters). We find that in moderation, adding multilingual data improves low-resource language modeling performance, similar to increasing low-resource dataset sizes by up to 33%. Improvements depend on the syntactic similarity of the added multilingual data, with marginal additional effects of vocabulary overlap. However, high-resource languages consistently perform worse in multilingual pre-training scenarios. As dataset sizes increase, adding multilingual data begins to hurt performance for both low-resource and high-resource languages, likely due to limited model capacity (the "curse of multilinguality"). These results suggest that massively multilingual pre-training may not be optimal for any languages involved, but that more targeted models can significantly improve performance