Natural And Social Science Test Primary 4 Macmillan Rar

Humans are naturally social. Yet, the modern way of life in industrialized countries is greatly reducing the quantity and quality of social relationships. Many people in these countries no longer live in extended families or even near each other. Instead, they often live on the other side of the country or even across the world from their relatives. Many also delay getting married and having children. Likwise, more and more people of all ages in developed countries are living alone, and loneliness is becoming increasingly common. In the UK, according to a recent survey by the Mental Health Foundation, 10% of people often feel lonely, a third have a close friend or relative who they think is very lonely, and half think that people are getting lonelier in general. Similarly, across the Atlantic, over the past two decades there has been a three-fold increase in the number of Americans who say they have no close confidants. There is reason to believe that people are becoming more socially isolated.

Natural And Social Science Test Primary 4 Macmillan Rar

Data within studies were often reported in terms of odds ratios (ORs), the likelihood of mortality across distinct levels of social relationships. Because OR values cannot be meaningfully aggregated, all effect sizes reported within studies were transformed to the natural log OR (lnOR) for analyses and then transformed back to OR for interpretation. When effect size data were reported in any metric other than OR or lnOR, we transformed those values using statistical software programs and macros (e.g., Comprehensive Meta-Analysis [24]). In some cases when direct statistical transformation proved impossible, we calculated the corresponding effect sizes from frequency data in matrices of mortality status by social relationship status. When frequency data were not reported, we recovered the cell probabilities from the reported ratio and marginal probabilities. When survival analyses (i.e., hazard ratios) were reported, we calculated the effect size from the associated level of statistical significance, often derived from 95% confidence intervals (CIs). Across all studies we assigned OR values less than 1.00 to data indicative of increased mortality and OR values greater than 1.00 to data indicative of decreased mortality for individuals with relatively higher levels of social relationships.

When multiple reports contained data from the same participants (publications of the same database), we selected the report containing the whole sample and eliminated reports of subsamples. When multiple reports contained the same whole sample, we selected the one with the longest follow-up duration. When multiple reports with the same whole sample were of the same duration, we selected the one reporting the greatest number of measures of social relationships.

In cases where multiple effect sizes were reported across different levels of social relationships (i.e., high versus medium, medium versus low), we extracted the value with the greatest contrast (i.e., high versus low). When a study contained multiple effect sizes across time, we extracted the data from the longest follow-up period. If a study used statistical controls in calculating an effect size, we extracted the data from the model utilizing the fewest statistical controls so as to remain as consistent as possible across studies (and we recorded the type and number of covariates used within each study to run post hoc comparative analyses). We coded the research design used rather than estimate risk of individual study bias. The coding protocol is available from the authors.

Metaregression is an analogue to multiple regression analysis for effect sizes. Its primary purpose is to ascertain which continuous and categorical (dummy coded) variables predict variation in effect size estimates. Using random effects weighted metaregression, we examined the simultaneous association (with all variables entered into the model) between effect sizes and prespecified participant and study characteristics (Table 3). To examine the most precise effect size estimates available and to increase the statistical power associated with this analysis, we shifted the unit of analysis [24] and extracted effect sizes within studies that were specific to measures of structural aspects of social relationships. That is, if a study contained effect sizes from both structural and functional types of social relationships, we extracted the structural types for this analysis (with identical subtypes aggregated), which resulted in a total of 230 unique effect sizes across 116 studies. A total of 18% of the variance in these effect sizes was explained in the metaregression (p

Notably, the overall effect for social relationships on mortality reported here may be a conservative estimate. Many studies included in the meta-analysis utilized single item measures of social relations, yet the magnitude of the association was greatest among those studies utilizing complex assessments. Moreover, because many studies statistically adjusted for standard risk factors, the effect may be underestimated, since some of the impact of social relationships on mortality may be mediated through such factors (e.g., behavior, diet, exercise). Additionally, most measures of social relations did not take into account the quality of the social relationships, thereby assuming that all relationships are positive. However, research suggests this is not the case, with negative social relationships linked to greater risk of mortality [184],[185]. For instance, marital status is widely used as a measure of social integration; however, a growing literature documents its divergent effects based on level of marital quality [186],[187]. Thus the effect of positive social relationships on risk of mortality may actually be much larger than reported in this meta-analysis, given the failure to account for negative or detrimental social relationships within the measures utilized across studies.

Finally, in addition to the impacts of the incarceration environment and healthcare provision there is a lack of educational and discharge planning programs in correctional settings. While prison education programs once were widely available, the elimination of prisoner eligibility for Federal Pell education grants in 1994 caused participation in postsecondary correctional education programs to decrease 44% (Crayton and Neusteter 2008). Most prisons still have correctional education programs, but only one-third of prisoners who are released will have participated in some type of work training or educational programming while incarcerated (Crayton and Neusteter 2008; Petersilia 2008). Additionally, the rate of participation in education programs offered in correctional facilities has not grown proportionate in comparison to the prison population as a whole (Western et al. 2003). Rather, the percentage of participation in educational programs has gradually decreased over the years (U.S. Department of Justice, Bureau of Justice Statistics 2007; Crayton and Neusteter 2008; Harlow 2003). This decline is relevant in that a large body of research demonstrates the efficacy of educational programming in significantly decreasing the likelihood of recidivism (Chappell 2004; Flinchum et al. 2006). Additionally, incarcerated individuals are much more likely to have lower levels of education than the general population, but incarcerated populations tend to have higher literacy scores than their counterparts in the general population. This points to the fact that for systematically disenfranchised populations, prisons may be the primary route to obtaining educational opportunities. Upon release, such educational experiences are important predictors of well being, as socioeconomic status is one of the strongest social determinants of health. However, even though over 93% of correctional leaders support the offering of educational and vocational opportunities in prisons, the increasingly punitive carceral environment has led to the deterioration of educational opportunities for correctional populations (Tyler et al. 2006).

A large amount of research has investigated the importance of social support and ties, especially within vulnerable populations (Karb et al. 2013; Knowlton 2003). Social support has been shown to mediate engagement in risky behavior and serve as a facilitator of individual and collective empowerment (Gabriel 2007; Lauby et al. 2012). Additionally, research has demonstrated the link between social support and health, indicating that higher levels of social support lead to more positive health outcomes (Sarason et al. 2010). However, in contrast, several policies regarding prisons actively sever social relationships. This loss of social support during incarceration can extend to the post-release period and can negatively affect health. For instance, Khan et al. (2011) found that engagement in primary partnerships might decrease sexual risk-taking among men involved in the criminal justice system but that 55% reported that their relationships ended during incarceration. A lack of social support can also negatively influence reintegration after release. Binswanger et al. (2012) found that lack of social support resulting in feelings of isolation often led to an increased likelihood of a reluctant return to alcohol and drug use.

Barriers are also in place that prohibit access to federal aid for higher education. The Higher Education Act (1998) states that those with drug possession convictions are no longer eligible for federally supplemented aid for one year after a first conviction, two years after their second conviction, and indefinitely after their third. This penalty is even harsher for an individual who is convicted of selling drugs. If convicted for selling drugs, the offender is ineligible for education assistance for two years after their first offense and completely ineligible aft