Do nations' mental health policies, programs and legislation influence their suicide rates? An ecological study of 100 countries

Objective: To test the hypothesis that the presence of national mental health policies, programs and legislation would be associated with lower national suicide rates. Method: Suicide rates from 100 countries were regressed on mental health policy, program and legislation indicators. Results: Contrary to the hypothesized relationship, the study found that after introducing mental health initiatives (with the exception of substance abuse policies), countries' suicide rates rose. Conclusion: It is of concern that most mental health initiatives are associated with an increase in suicide rates. However, there may be acceptable reasons for the observed findings, for example initiatives may have been introduced in areas of increasing need, or a case-finding effect may be operating. Data limitations must also be considered.

Exclusionary DNA of forensic workers and Australian forensic procedures legislation

Consideration of regulatory issues covering exclusionary DNA of forensic workers - probative effect of eliminating extraneous DNA in a criminal prosecution - current regulatory scheme leaves the legal position of forensic workers' exclusionary DNA obscure.

Does being against euthanasia legislation equate to being anti-euthanasia?

This study investigated issues raised in qualitative data from our previous studies of health professionals and community members, which suggested that being opposed to euthanasia legislation did not necessarily equate to being anti-euthanasia per se. A postal survey of 1002 medical practitioners, 1000 nurses and 1200 community members was undertaken. In addition to a direct question on changing the law to allow active voluntary euthanasia (AVE), four statements assessed attitudes to euthanasia with or without a change in legislation. Responses were received from 405 doctors (43%), 429 nurses (45%) and 405 community members (38%). Compared with previous studies there was a slight increase in support for a change in the law from medical practitioners, a slight decrease in support from community members and almost no change among nurses. Different interpretations of the results of the four attitude questions are possible, depending on the perspective of the interpreter.

Multi-topic information filtering with a single user profile

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.