Managing women with acute physiological deterioration: student midwives performance in a simulated setting

OBJECTIVE: Midwives' ability to manage maternal deterioration and 'failure to rescue' are of concern with questions over knowledge, clinical skills and the implications for maternal morbidity and, mortality rates. In a simulated setting our objective was to assess student midwives' ability to assess, and manage maternal deterioration using measures of knowledge, situation awareness and skill, performance. METHODS: An exploratory quantitative analysis of student performance based upon performance, ratings derived from knowledge tests and observational ratings. During 2010 thirty-five student, midwives attended a simulation laboratory completing a knowledge questionnaire and two video, recorded simulated scenarios. Patient actresses wearing a 'birthing suit' simulated deteriorating, women with post-partum and ante-partum haemorrhage (PPH and APH). Situation awareness was, measured at the end of each scenario. Applicable descriptive and inferential statistical tests were, applied to the data. FINDINGS: The mean total knowledge score was 75% (range 46-91%) with low skill performance, means for both scenarios 54% (range 39-70%). There was no difference in performance between the scenarios, however performance of key observations decreased as the women deteriorated; with significant reductions in key vital signs such as blood pressure and blood loss measurements. Situation, awareness scores were also low (54%) with awareness decreasing significantly (t(32)=2.247, p=0.032), in the second and more difficult APH scenario. CONCLUSION: Whilst knowledge levels were generally good, skills were generally poor and decreased as the women deteriorated. Such failures to apply knowledge in emergency stressful situations may be resolved by repetitive high stakes and high fidelity simulation.

Fighting fire and fatigue: sleep quantity and quality during multi-day wildfire suppression

This study examined firefighters' sleep quantity and quality throughout multi-day wildfire suppression, and assessed the impact of sleep location, shift length, shift start time and incident severity on these variables. For 4 weeks, 40 volunteer firefighters' sleep was assessed using wrist actigraphy. Analyses revealed that the quantity of sleep obtained on fire days was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. On fire days, total sleep time was less when: (i) sleep location was in a tent or vehicle, (ii) shifts were greater than 14 h and (iii) shifts started between 05:00 and 06:00 h. This is the first empirical investigation providing objective evidence that firefighters' sleep is restricted during wildfire suppression. Furthermore, sleep location, shift length and shift start time should be targeted when designing appropriate controls to manage fatigue-related risk and preserve firefighters' health and safety during wildfire events. Practitioner Summary: During multi-day wildfire suppression, firefighters' sleep quantity was restricted, and pre- and post-sleep fatigue ratings were higher, compared to non-fire days. Furthermore, total sleep time was less when: (i) sleep occurred in a tent/vehicle, (ii) shifts were >14 h and (iii) shifts started between 05:00 and 06:00 h.

Cultural intelligence (CQ) in virtual, cross-cultural interactions: generalizability of measure and links to personality dimensions and task performance

Previous studies have focused on investigating CQ in face-to-face contexts but very few have assessed CQ in virtual, cross-cultural interactions. This study highlights the relevance of cultural intelligence (CQ) as an intercultural capability in cross-cultural communications that are virtual. This two-study research (study 1: n = 274; study 2: n = 223) conducted in call centers in the Philippines (a) assesses the generalizability of the four-factor CQ model (i.e., cognitive, metacognitive, motivational and behavioral CQ) as applied in the virtual context and (b) tests the relationship between CQ, personality dimensions (i.e., openness to experience and extraversion) and supervisor’s ratings of task performance. Study 1 results show that the structural validity of the four-factor CQ model was supported with minor issues in some ofthe items indicating the need to modify the CQ measure when utilized in the virtual context. Study 2 results show that CQ is positively and significantly related to openness to experience and extraversion. In addition, results show that CQ predicts task performance highlighting the importance of developing CQ among call center representatives and other working professionals who virtually engage and interact with clients and customers from culturally diverse backgrounds.

Subtyping based on readiness and confidence: the identification of help-seeking profiles for gamblers accessing web-based counselling

BACKGROUND AND AIMS: Problem gamblers are not a homogeneous group and recent data suggest that subtyping can improve treatment outcomes. This study administered three readiness rulers and aimed to identify subtypes of gamblers accessing a national web-based counselling service based on these rulers. METHODS: Participants were 1204 gamblers (99.4% problem gamblers) who accessed a single session of web-based counselling in Australia. Measures included three readiness rulers (importance, readiness and confidence to resist an urge to gamble), demographics and the Problem Gambling Severity Index (PGSI). RESULTS: Gamblers reported high importance of change [mean = 9.2, standard deviation (SD) = 1.51] and readiness to change (mean = 8.86, SD = 1.84), but lower confidence to resist an urge to gamble (mean = 3.93, SD = 2.44) compared with importance and readiness. The statistical fit indices of a latent class analysis identified a four-class model. Subtype 1 was characterized by a very high readiness to change and very low confidence to resist an urge to gamble (n = 662, 55.0%) and subtype 2 reported high readiness and low confidence (n = 358, 29.7%). Subtype 3 reported moderate ratings on all three rulers (n = 139, 11.6%) and subtype 4 reported high importance of change but low readiness and confidence (n = 45, 3.7%). A multinomial logistic regression indicated that subtypes differed by gender (P < 0.001), age (P = 0.01), gambling activity (P < 0.05), preferred mode of gambling (P < 0.001) and PGSI score (P < 0.001). CONCLUSIONS: Problem gamblers in Australia who seek web-based counselling comprise four distinct subgroups based on self-reported levels of readiness to change, confidence to resist the urge to gamble and importance of change.

Intensive case management for high-risk patients with first-episode psychosis: service model and outcomes

Background : The first episode of psychosis is a crucial period when early intervention can alter the trajectory of the young person's ongoing mental health and general functioning. After an investigation into completed suicides in the Early Psychosis Prevention and Intervention Centre (EPPIC) programme, the intensive case management subprogramme was developed in 2003 to provide assertive outreach to young people having a first episode of psychosis who are at high risk owing to risk to self or others, disengagement, or suboptimal recovery. We report intensive case management model development, characterise the target cohort, and report on outcomes compared with EPPIC treatment as usual.

Methods : Inclusion criteria, staff support, referral pathways, clinical review processes, models of engagement and care, and risk management protocols are described. We compared 120 consecutive referrals with 50 EPPIC treatment as usual patients (age 15–24 years) in a naturalistic stratified quasi-experimental real-world design. Key performance indicators of service use plus engagement and suicide attempts were compared between EPPIC treatment as usual and intensive case management, and psychosocial and clinical measures were compared between intensive case management referral and discharge.

Findings : Referrals were predominately unemployed males with low levels of functioning and educational attainment. They were characterised by a family history of mental illness, migration and early separation, with substantial trauma, history of violence, and forensic attention. Intensive case management improved psychopathology and psychosocial outcomes in high-risk patients and reduced risk ratings, admissions, bed days, and crisis contacts.

Interpretation : Characterisation of intensive case management patients validated the clinical research focus and identified a first episode of psychosis high-risk subgroup. In a real-world study, implementation of an intensive case management stream within a well-established first episode of psychosis service showed significant improvement in key service outcomes. Further analysis is needed to determine cost savings and effects on psychosocial outcomes. Targeting intensive case management services to high-risk patients with unmet needs should reduce the distress associated with pathways to care for patients, their families, and the community.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

Learning preference models from human generated data is an important task in modern information processing systems. Its popular setting consists of simple input ratings, assigned with numerical values to indicate their relevancy with respect to a specific query. Since ratings are often specified within a small range, several objects may have the same ratings, thus creating ties among objects for a given query. Dealing with this phenomena presents a general problem of modelling preferences in the presence of ties and being query-specific. To this end, we present in this paper a novel approach by constructing probabilistic models directly on the collection of objects exploiting the combinatorial structure induced by the ties among them. The proposed probabilistic setting allows exploration of a super-exponential combinatorial state-space with unknown numbers of partitions and unknown order among them. Learning and inference in such a large state-space are challenging, and yet we present in this paper efficient algorithms to perform these tasks. Our approach exploits discrete choice theory, imposing generative process such that the finite set of objects is partitioned into subsets in a stagewise procedure, and thus reducing the state-space at each stage significantly. Efficient Markov chain Monte Carlo algorithms are then presented for the proposed models. We demonstrate that the model can potentially be trained in a large-scale setting of hundreds of thousands objects using an ordinary computer. In fact, in some special cases with appropriate model specification, our models can be learned in linear time. We evaluate the models on two application areas: (i) document ranking with the data from the Yahoo! challenge and (ii) collaborative filtering with movie data. We demonstrate that the models are competitive against state-of-the-arts.

Privacy preserving collaborative filtering for KNN attack resisting

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNN attack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering (PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCF contains an essential privacy operation, Private Neighbor Selection, in which the Laplace noise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivity and a re-designed truncated similarity are introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attack while retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCF algorithm retains most of the utility with a fixed privacy budget.

An assessment of criteria to evaluate the graduate education program in Brazil

This study sought to identify criteria adequate for the evaluation of graduate programs in Brazil. A survey was the means for collecting the ratings and rankings given by faculty members at selected Brazilian graduate programs. A questionnaire using Likerttype and ranking items asked the importance attributed by each respondent to each of the 109 items listed. The data analysis reported in this dissertation indicates that the most highly rated criteria and indicators were: (1) Library: current periodicals; (2) Facilities: classrooms and laboratories; (3) Library: books and monographs; (4) Academic Environment: discussion, investigation, and expression; and (5) Facilities: research space and equipment. The study presents the means and standard deviations obtained for each indicator and also includes some figures obtained for a relational analysis. This dissertation was developed to provide useful information to educational planners, policy makers, administrators, and evaluators involved in Brazilian higher education or comparative studies. It is suggested that additional investigations concentrate on more specific and in-depth analysis and interpretation of the policymaking processes, i.e., on the study of social facts or organizational and academic variables in their relationships with aspects of the educational system. The appendices section includes a facsimile of the questionnaire and additional data.

Basiléia II e exigência de capital para risco de crédito dos bancos no Brasil

Com a implementação do Acordo de Basiléia II no Brasil, os grandes conglomerados bancários poderão utilizar o chamado modelo IRB (Internal Ratings Based) para cômputo da parcela de risco de crédito da exigência de capital. O objetivo deste trabalho é mensurar a diferença entre o capital mínimo exigido (e, conseqüentemente, do Índice de Basiléia) calculado pela abordagem IRB em relação à regulamentação atual. Para isso, foram estimadas probabilidades de inadimplência (PD) utilizando matrizes de transição construídas a partir dos dados da Central de Risco de Crédito (SCR) do Banco Central do Brasil. Os resultados indicam aumento da exigência de capital, ao contrário do ocorrido nos países do G-10

Sale of credit portfolio and risk : the case of financial institutions in Brazil

Este estudo analisa se as vendas de carteiras de crédito são utilizadas por instituições financeiras para gestão de risco, de acordo com Stanton(1998) e Murray(2001) ou para captação recursos, como apontado em Cebenoyan e Strahan(2001) e Dionne e Harchaoui(2003). Duas hipóteses foram testadas quanto às vendas de carteira de crédito: 1) implicam em melhor rating na carteira remanescente; ou 2) promovem alavancagem financeira - com piora na carteira remanescente -, controlando para a existência de coobrigação e para quem esses ativos foram transferidos. A amostra inclui informações trimestrais de 145 instituições financeiras do primeiro trimestre de 2001 ao segundo trimestre de 2008. Os resultados oferecem evidências empíricas de que as instituições financeiras utilizam estas vendas para melhora do rating da carteira de crédito remanescente, ou seja, elas transferem, em sua maioria, ativos de baixa qualidade, garantindo bons ratings e melhorando a liquidez. Adicionalmente, seguindo a proposta Dionne e Harchaoui(2003) - que além de testar, evidenciam que exigências regulatórias promovem alavancagem em ativos de alto risco - foi observada a relação entre o Índice de Basiléia e rating da carteira de crédito. As conclusões foram semelhantes às encontradas por Dionne e Harchaoui(2003): quanto mais adequada – maior Índice de Basiléia - uma instituição financeira for, maiores as chances de ela possuir uma carteira de crédito com qualidade ruim.

Performance de fundos mútuos de investimento no Brasil : uma análise dos fenomenos de "hot-hands", sinalização e persistência de retornos

o presente trabalho apresenta um resumo não só dos conceitos básicos sobre fundos de investimento, como também seu histórico e evolução no Brasil e no mundo, o contexto institucional nacional e os principais ratings de classificação de performance. Sua parte empírica foca-se no exame do fenômeno de persistência de performance para os fundos de investimentos ativos brasileiros entre julho de 1996 e junho de 2001. o estudo empírico dividiu-se em três partes: a comparação do retomo das categorias de fundos de ações ativos, de renda fixa e multimercados com os respectivos benchmarks e fundos passivos; a verificação do fenômeno de persistência baseando-se em Malkiel (1995); e a análise dos resultados de investimentos em portfólios formados pelos grupos de uma mesma categoria pertecentes a um mesmo percentil de retomo não ajustado ao risco.

Ensaios sobre emissões de corporate bonds (Debêntures) no mercado brasileiro

O objetivo da presente tese é analisar as três questões não respondidas ou tratadas na literatura nacional - os efeitos de rating, as medidas e proxies de liquidez e os desenhos de contratos nas emissões de diferentes ratings -, que ajudam a compreender a formação de taxa de juros de emissão de debêntures. Para alcançar esse objetivo, esta tese está estruturada em três ensaios empíricos. No ensaio de rating, encontraram-se evidências de que: i) o rating afeta o spread independentemente do indexador da emissão; ii) a origem - nacional ou internacional - dos ratings não é relevante; iii) a causalidade rating-spread não é clara nas emissões que ocorrem em ambiente favorável, em que se verifica maior incidência de classificações discrepantes de risco; iv) a expectativa do mercado internacional com relação ao ambiente econômico brasileiro, o tipo de setor e o volume da emissão são importantes variáveis de controle na determinação do spread. No ensaio de liquidez, foram obtidos os seguintes resultados: i) o tamanho de emissão e determinados tipos de emissores, como, por exemplo, setores de energia e petróleo, são proxies de liquidez; ii) controlando determinados tipos de emissores, as debêntures com maior tamanho de emissão são as mais líquidas; iii) a relação entre idade e liquidez não é clara; iv) a diferença entre preços máximos e mínimos das transações não é uma medida de liquidez apropriada. Por fim, no ensaio de contrato, foram detectadas diferenças entre as cláusulas de emissões de diferentes ratings; conseqüentemente, a padronização afeta a taxa de juros das emissoras, uma vez que o rating é um dos principais determinantes da taxa de juros de emissão. Além disso, a padronização pode não ser ótima nas emissões de baixo rating, pois os contratos-padrão não incluem cláusulas restritivas, tais como repactuação programada e garantia, que reduzem o custo de agência entre acionistas e debenturistas.

Modelo interno de risco de crédito de Basiléia II: possíveis impactos no capital mínimo exigido dos bancos

Com a implementação do Acordo de Basiléia II no Brasil, os grandes conglomerados bancários poderão utilizar o chamado modelo IRB (Internal Ratings Based) para cômputo da parcela de risco de crédito da exigência de capital. O objetivo desta dissertação é mensurar a diferença entre o capital mínimo exigido (e, conseqüentemente, do Índice de Basiléia) calculado pela abordagem IRB em relação à regulamentação atual. Para isso, foram estimadas probabilidades de inadimplência (PD) utilizando matrizes de transição construídas a partir dos dados da Central de Risco de Crédito (SCR) do Banco Central do Brasil. Os resultados indicam aumento da exigência de capital, ao contrário do ocorrido nos países do G-10.