Jurnal STAI Al-Hamidiyah
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Assessing Decision-Making Tools and Conversation Guides for Meaningful Discussions about Transfer Decisions from Long-Term Care Facilities to the Hospital: A Scoping Review
No full textThis scoping review aims to identify, evaluate, and map existing decision-making tools and conversation guides for transfer decisions in LTC facilities to hospitals, assess their efficacy, analyze their key components, identify gaps in the literature, and provide recommendations for improvement
Risk of early language deprivation among deaf bilinguals with varying early signing experience: evidence from American Sign Language basic word order and bilingual proficiency
No full textStrategic Heterogeneity in Policy-Level Positioning: Evidence from Congressional Campaigns
No full textDo candidates maintain consistent left-right issue positions in congressional elections? How do district conditions influence variations in this consistency? While these questions are fundamental to the study of U.S. elections, surprisingly little is known about issue positioning in campaigns. We combine campaign platforms with machine learning methods to estimate the left-right orientations of U.S. House candidates (n = 4,505) across six salient issue areas in multiple election cycles (2018-2022). Our validated measures reveal meaningful within-candidate variation in issue positioning that systematically reflects district conditions. Candidates exhibit greater positional flexibility in districts that are not safe for their party and where constituent policy preferences are heterogeneous. We further demonstrate that this positional variation directly reflects district opinion, as issue-specific positioning aligns with constituency policy preferences. These findings reveal how candidate messaging varies systematically across issue domains and electoral contexts, fundamentally shaping the information environment that structures democratic choice and representation
Report_2021_12
No full textCosta C, 2021. Vademecum per le attivita’ di supporto al monitoraggio ambientale e gestione dei corpi idrici superficiali e delle acque costiere. CURSA: 1-18
Individual differences in affective processing in laboratory rats and implications for animal welfare
No full textThis study will investigate how the way in which an animal processes affective information influences its welfare in the face of changing opportunities and challenges. Recent research using the translational judgement bias task developed in our lab and designed to assess animal affective (emotional) state (JBT; >200 published studies), indicates that individuals vary consistently in whether they anticipate positive or negative outcomes when making decisions under uncertainty. In humans, such individual differences in affective processing (‘optimism’ / ‘pessimism’) are known to predict emotional state, wellbeing and vulnerability to affective disorders such as depression, and in non-human animals there is early evidence that ‘pessimistic’ phenotypes may indeed be more vulnerable to stress.
In this study, we will test the hypothesis that personality differences in affective processing have an impact on an animal’s affective state and welfare. We will use a reaction norm approach that allows us to quantify not just individual differences in mean responses (personality), but also individual differences in how responses change across context / time (plasticity) and in the variability of responding within a given context (predictability). We will apply this approach to parameters extracted from JBT data using computational modelling which characterises aspects of affective processing such as optimism-bias and reward/punishment sensitivity. These parameters are known to play a role in how individuals respond to challenge. We will work with laboratory rats, the species we used to develop the original JBT, and we will study both sexes. Sex bias in favour of males remains pervasive in many studies partly because it is assumed that females, due to the oestrous cycle, produce more variable data. Our predictability readouts will address this issue directly.
Once individuals have been characterised, we will monitor their welfare in contexts that vary in opportunity and threat, generating a set of reaction norms that summarise how their welfare changes with changing availability of opportunity (enrichment provision) and changing presence of threat (unpredictable events). We will investigate links between individual affective processing characteristics and welfare reaction norms to test hypotheses including whether more ‘optimistic’ animals, especially those showing predictable rather than variable levels of ‘optimism’, cope better with challenge (e.g. show flattening of reaction norms across contexts varying in threat).
Objectives:
• Objective i: to use repeated JBT tests to quantify individual differences in personality, plasticity, and predictability of extracted affective processing parameters such as optimism-bias and reward/punishment sensitivity in male and female rats
• Objective ii: to use welfare indicators to measure individual welfare reaction norms across environments varying in opportunity and threat
• Objective iii: to evaluate whether and how individual affective processing characteristics measured in Obj. i predict welfare reaction norms measured in Obj. ii
How much could reducing socioeconomic disadvantage prevent inequities in child language?
No full textBackground: Language outcomes have a social gradient, whereby inequities are driven by socioeconomic resources. These socioeconomic inequities are unjust and avoidable. Few studies have estimated the potential effect on later language outcomes across the population if we were to reduce levels of socioeconomic disadvantage.
Objectives: Here we will simulate nine hypothetical interventions that reduce socioeconomic disadvantage. We will estimate the potential reduction in cases of low language across the population afforded by each hypothetical intervention.
Methods: We will use a causal inference approach and target trial framework. The sample will comprise the 5,107 children recruited into the Longitudinal Study of Australian Children (LSAC) birth cohort in 2003-2004. We will simulate hypothetical interventions targeting three indicators of socioeconomic position: (1) household income, (2) primary parents’ education, (3) neighbourhood disadvantage levels, each measured at child age 0-1 years. For each indicator we will simulate three scenarios where socioeconomic disadvantage is increasingly reduced. We operationalise ‘low language’ as >1.5 SD below the mean of the Clinical Evaluation of Language Fundamentals, Fourth Edition (CELF-4) Recalling Sentences scaled score at 11-12 years. We will use an interventional effects approach and an extended g-computation estimation procedure to estimate the extent to which socioeconomic inequities in Recalling Sentences scores could be reduced across the population by each hypothetical intervention.
Expected results and outcomes: Our results will estimate the potential population-wide effects of reducing inequalities in three socioeconomic indicators on children’s long-term language outcome. This could inform policy decisions aimed at reducing inequities in children’s language outcomes
Scaffolding Shared Reality: The Role of Friendship Ties and Mental Health on Agreement in Recalled Social Groups
No full textExperiences and perceptions of functional recovery in late-life depression: a qualitative study
No full textDepression is one of the most prevalent disorders worldwide. Although the burden of disease of depression is higher amongst working ages, late-life depression is still responsible for 6.1 million years of life lived with disability (YLDs) (Ferrari et al., 2013). The latest Global Burden of Disease study (2019) described that there has been an increase of 7.3% in DALYs due to depression in older adults since 1990 (Vos et al., 2020). This is not expected to decrease due the aging population (Heo et al., 2008; Wassink-Vossen et al., 2022). Late-life depression is characterised by a more adverse prognosis with increasing age (Comijs et al., 2015; Schaakxs et al., 2018). Late-life depression might cause more physical, psychological, and social dysfunction when compared to younger adults. Although late-life depression is not more prevalent than depression in younger adults, the elderly suffer frequently from minor and subclinical depression. Furthermore, late-life depression is frequentely chronical or recurrently present (Blazer, 2003). Additionally, late-life depression is associated with cognitive deterioration and dementia (Riddle et al., 2017). Moreover, it is associated with more functional decline (Collard et al., 2018). Previous research shows that 80% of late-life depressed elderly do not recover functionally after two years (Collard et al., 2018; Lenze et al., 2005; Wassink-Vossen et al., 2019). Kamenov et al (2015) reported that 80% of all depression studies are focussing on symptoms (Kamenov et al., 2015), while patients have stated that their ability to function in daily life is most important to them (Bickenbach, 2012; Zimmerman et al., 2006). Current treatment exists of medication and psychotherapy. Although there are few studies comparing the effect of treatment between elderly and younger adults, some studies show that the effectiveness of antidepressants is lower in elderly (Gould et al., 2012; Tedeschini et al., 2011). Although psychopharmacological treatment is necessary, 50% of late-life depressed patient have relapses or recurrent episodes (Comijs et al., 2011). Therefore, it is necessary to increase understanding of functional recovery and to gain more insight into important themes in recovery from late-life depression. The aim is to achieve this through qualitative research that will focus on exploring the experiences and perceptions of elderly patients in the functional recovery process of late-life depression
A Quantum Model for Serial Reproduction
No full textWe propose a novel quantum walk framework for modeling serial reproduction, designed to effectively capture the transmission of inherently vague concepts, such as emotions and ideas, through quantum superpositions and controlled unitary operations. In a comprehensive comparison with Bayesian models using the largest dataset of reproduced narratives to date, our framework demonstrates superior predictive accuracy, surpassing Bayesian approaches in modeling non-linear relationships and multimodal distributions in emotion transmission. Furthermore, it successfully replicates recent key findings on emotion transmission in serial reproduction