Consistencies and Inconsistencies: Mental Health, Compulsory Treatment and Community Capacity Building in England, Wales and Australia

In relation to mental health, it is clear that medicalized underpinnings,the provision of a clinically orientated evidence base for practiceand the need to contain ‘risk’ continue to be prioritized,particularly when compulsory intervention is considered. Thisis despite the increasing influence of a citizenship and socialjustice agenda which is being promoted by psychiatrists involvedin the development of ‘postpsychiatry’, as wellas by service users/survivors. These drivers are not mutuallyexclusive, but in the context of compulsory treatment, thereare clear tensions. In this article, the policy surroundingmental health and compulsory intervention in Australia and inEngland and Wales is explored. In the context of this discussion,three ways in which current government policy can be interpretedare examined and links made between these interpretations anddifferent ways of viewing the relationship between mental healthand community capacity building. With regard to social workpractice, it is argued that a response to mental ill healthwhich concentrates on an individualized ‘diagnose andtreat’ approach, which particularly comes to the foreat times of compulsory intervention, has limited capacity tofacilitate community engagement, foster social inclusion andgenerate reciprocal dynamics between positive mental healthand community capacity building.     

On an adaptive version of the Metropolis–Hastings algorithm with independent proposal distribution

In this paper, we present a general formulation of an algorithm, the adaptive independent chain (AIC), that was introduced in a special context in Gåsemyr et al . [ Methodol. Comput. Appl. Probab. 3 (2001)]. The algorithm aims at producing samples from a specific target distribution Π, and is an adaptive, non-Markovian version of the Metropolis–Hastings independent chain. A certain parametric class of possible proposal distributions is fixed, and the parameters of the proposal distribution are updated periodically on the basis of the recent history of the chain, thereby obtaining proposals that get ever closer to Π. We show that under certain conditions, the algorithm produces an exact sample from Π in a finite number of iterations, and hence that it converges to Π. We also present another adaptive algorithm, the componentwise adaptive independent chain (CAIC), which may be an alternative in particular in high dimensions. The CAIC may be regarded as an adaptive approximation to the Gibbs sampler updating parametric approximations to the conditionals of Π.     

Modeling External Risks in Project Management

To ascertain the viability of a project, undertake resource allocation, take part in bidding processes, and other related decisions, modern project management requires forecasting techniques for cost, duration, and performance of a project, not only under normal circumstances, but also under external events that might abruptly change the status quo. We provide a Bayesian framework that provides a global forecast of a project's performance. We aim at predicting the probabilities and impacts of a set of potential scenarios caused by combinations of disruptive events, and using this information to deal with project management issues. To introduce the methodology, we focus on a project's cost, but the ideas equally apply to project duration or performance forecasting. We illustrate our approach with an example based on a real case study involving estimation of the uncertainty in project cost while bidding for a contract.     

A Bayesian Hierarchical Model for the Evaluation of a Website

Consider a website and the surfers visiting its pages. A typical issue of interest, for example while monitoring an advertising campaign, concerns whether a specific page has been designed successfully, i.e. is able to attract surfers or address them to other pages within the site. We assume that the surfing behaviour is fully described by the transition probabilities from one page to another, so that a clickstream (sequence of consecutively visited pages) can be viewed as a finite-state-space Markov chain. We then implement a variety of hierarchical prior distributions on the multivariate logits of the transition probabilities and define, for each page, a content effect and a link effect. The former measures the attractiveness of the page due to its contents, while the latter signals its ability to suggest further interesting links within the site. Moreover, we define an additional effect, representing overall page success, which incorporates both effects previously described. Using WinBUGS, we provide estimates and credible intervals for each of the above effects and rank pages accordingly.     

A Non-parametric Frailty Model for Temporally Clustered Multivariate Failure Times

Abstract A model is introduced here for multivariate failure time data arising from heterogenous populations. In particular, we consider a situation in which the failure times of individual subjects are often temporally clustered, so that many failures occur during a relatively short age interval. The clustering is modelled by assuming that the subjects can be divided into ‘internally homogenous’ latent classes, each such class being then described by a time‐dependent frailty profile function. As an example, we reanalysed the dental caries data presented earlier in Härkänen et al. [Scand. J. Statist. 27 (2000) 577], as it turned out that our earlier model could not adequately describe the observed clustering.     

Summary of the Workshop on Issues in Risk Assessment: Quantitative Methods for Developmental Toxicology

This report summarizes the proceedings of a conference on quantitative methods for assessing the risks of developmental toxicants. The conference was planned by a subcommittee of the National Research Council's Committee on Risk Assessment Methodology ⁴ in conjunction with staff from several federal agencies, including the U.S. Environmental Protection Agency, U.S. Food and Drug Administration, U.S. Consumer Products Safety Commission, and Health and Welfare Canada. Issues discussed at the workshop included computerized techniques for hazard identification, use of human and animal data for defining risks in a clinical setting, relationships between end points in developmental toxicity testing, reference dose calculations for developmental toxicology, analysis of quantitative dose-response data, mechanisms of developmental toxicity, physiologically based pharmacokinetic models, and structure-activity relationships. Although a formal consensus was not sought, many participants favored the evolution of quantitative techniques for developmental toxicology risk assessment, including the replacement of lowest observed adverse effect levels (LOAELs) and no observed adverse effect levels (NOAELs) with the benchmark dose methodology.     

Uncertain Numbers and Uncertainty in the Selection of Input Distributions—Consequences for a Probabilistic Risk Assessment of Contaminated Land

Risks from exposure to contaminated land are often assessed with the aid of mathematical models. The current probabilistic approach is a considerable improvement on previous deterministic risk assessment practices, in that it attempts to characterize uncertainty and variability. However, some inputs continue to be assigned as precise numbers, while others are characterized as precise probability distributions. Such precision is hard to justify, and we show in this article how rounding errors and distribution assumptions can affect an exposure assessment. The outcome of traditional deterministic point estimates and Monte Carlo simulations were compared to probability bounds analyses. Assigning all scalars as imprecise numbers (intervals prescribed by significant digits) added uncertainty to the deterministic point estimate of about one order of magnitude. Similarly, representing probability distributions as probability boxes added several orders of magnitude to the uncertainty of the probabilistic estimate. This indicates that the size of the uncertainty in such assessments is actually much greater than currently reported. The article suggests that full disclosure of the uncertainty may facilitate decision making in opening up a negotiation window. In the risk analysis process, it is also an ethical obligation to clarify the boundary between the scientific and social domains.     

Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

Retirement patterns in Hong Kong: a censored regression analysis

This paper provides an overview of retirement patterns in Hong Kong on the basis of limited data. A censored regression model is used to infer the retirement age from people‘s current retirement status and their current age. This model is equivalent to a restricted probit model, and the interpretation of parameters is straightforward. The results clearly show a negative income effect on the retirement decision. The retirement age seems to be positively related to lifetime earnings but negatively related to the rate of decline of earnings with age. JEL classification: C24, J14, J26 Received May 6, 1996 / Accepted February 5, 1997     

数字素养与农户收入差距:加剧还是缓解?——基于中西部地区的微观证据

数字经济时代,数字贫困问题对农户收入差距的影响引发新的关注。基于2021年中国农村经济与农村金融数据,采用再中心化响应函数(RIF)回归方法探究数字素养对农户收入差距的影响。研究发现:(1)数字素养显著扩大了农户收入差距,并具备明显的马太效应,高数字素养能够攫取更多数字红利,而低数字素养更容易陷入“数字贫困”状态,无法发挥出数字经济的增收效应。(2)应用数字素养会显著加剧农户收入差距,而通用数字素养对农户收入差距的影响并不显著。这种情况也得以印证,现阶段数字“接入鸿沟”已经得以解决,数字“应用鸿沟”差距逐渐凸显。(3)数字素养能够缩小农户财产性收入差距,但会显著扩大工资性收入差距与非农经营性收入差距。脱贫地区受到数字素养的马太效应更强,农户收入差距明显加剧。研究结论证实了我国农村内部存在明显的数字鸿沟,同时数字贫困问题加剧了农户收入差距。新发展阶段我国数字乡村战略的推进不仅要注重农村低收入群体以及弱势群体数字素养的培育,更要加快构建农村数字经济的包容性发展路径,助力农民农村共同富裕的实现。