Convergent and Discriminant Validity of the Barthel Index and the EQ-5D-3L When Used on Older People in a Rehabilitation Setting

This study compares the empirical performance of a commonly used functional-status measure, the Barthel Index (BI), to that of a widely used generic preference-based instrument, the EuroQoL-5-Dimensions 3 Level (EQ-5D-3L), in older people. Data from older people receiving rehabilitation services were used to test the validity of the BI and EQ-5D-3L. Convergent validity was investigated using Spearman’s correlation, exploratory factor analysis (EFA), scatter plots, Krippendorff’s alpha and modified Bland-Altman plots. Discriminant validity was examined using Kruskal Wallis tests, ceiling effects and EFA. A total of 1690 participants were included in the analysis. The BI total and EQ-5D-3L utility scores showed moderate correlation (r = 0.51; Krippendorff’s alpha = 0.52). Kendall’s Tau-B correlations between BI items and EQ-5D-3L dimensions measuring the same construct were weak to moderate (0.05 ≤ absolute r ≤ 0.54). In the EFA, some BI items cross-loaded onto the same factors as EQ-5D-3L dimensions, suggesting that the instruments were interrelated. The BI, however, focuses more on physical functioning, while the EQ-5D-3L measures broader wellbeing concepts. Both instruments showed good discriminant validity and would therefore be equally valuable for measuring subgroup differences. Researchers should consider using the BI in rehabilitation to capture more physical functioning-specific constructs not measured by the EQ-5D-3L.

VOCATIONALIZATION OF EDUCATION THROUGH RUSA

India has a population of over 1.35 billion and a workforce of 460 million. To be able to provide employment to such a large number of people is a challenge, which becomes even more daunting as the population grows by more than 1.6 % every year. Unemployment by the current daily status measure is 6.2% of the labour force (NSSO Round 2009-10), which amounts to 27.6 million. In terms of demographics, almost 35% of Indians are younger than 15 years of age, whilst 18% fall within the age group of 15-24. The median age of India is 24 years , making it one of the youngest populations in the world. 54% of India’s rural workforce is self-employed (mainly in agriculture, 39%, but a significant share in non-agriculture, 15%), many of whom remain very poor. In urban areas, the self-employed constitute 42.6% (NSSO 2007-08), among whom the incidence of poverty may be lower, but high enough to be a source of concern. Nearly 36% in rural areas are either agricultural or other labor, usually casual workers. There are a negligible number of regular employees in rural areas, and in urban areas only about 13% are regular employees, of which two-fifths are employed by the public sector. More than 90% of the labour force is employed in the unorganized sector, i.e. sectors which don’t offer social safety and other benefits of employment in the organized sector.

Collegiate and Professional Ice Hockey Athletic Trainers’ Hydration Practices and Knowledge: Part 1

Despite cool environments, ice hockey athletes are predisposed to hypohydration due to extensive equipment, high intensity activity, and high sweat rates. The purpose of this study was to determine hydration practices and awareness of fluid recommendations among athletic trainers (ATs) working with NCAA Division I, NCAA Division III, and professional ice hockey teams. Most professional ATs (90.1%) utilized at least one hydration status measure, while 61.7% of collegiate ATs did not measure hydration. Compared to Division I, more Division III ATs did not have electrolyte supplements (p < .001) and believed they did not have adequate access through budget/sponsorship (p < .001). Both professional (72.0%) and collegiate (53%) ATs reported athletes had open access to electrolyte supplements or gave them to any athlete who asked. Athletic trainers provided patient education and had fluids accessible to athletes. Areas to improve clinical practice include monitoring electrolyte supplement access and collegiate ATs assessing hydration status.

Comparison of staff and resident health status ratings in care homes

BackgroundMany care home residents cannot self-report their own health status. Previous studies have shown differences between staff and resident ratings. In 2012, we collected 10 168 pairs of health status ratings using the howRu health status measure. This paper examines differences between staff and resident ratings.MethodHowRu is a short generic person-reported outcome measure with four items: pain or discomfort (discomfort), feeling low or worried (distress), limited in what you can do (disability) and require help from others (dependence). A summary score (howRu score) is also calculated. Mean scores are shown on a 0–100 scale. High scores are better than low scores. Differences between resident and staff reports (bias) were analysed at the item and summary level by comparing distributions, analysing correlations and a modification of the Bland-Altman method.Results and conclusionsDistributions are similar superficially but differ statistically. Spearman correlations are between 0.55 and 0.67. For items, more than 92.9% of paired responses are within one class; for the howRu summary score, 66% are within one class. Mean differences (resident score minus staff score) on 0–100 scale are pain and discomfort (−1.11), distress (0.67), discomfort (1.56), dependence (3.92) and howRu summary score (1.26). The variation is not the same for different severities. At higher levels of pain and discomfort, staff rated their discomfort and distress as better than residents. On the other hand, staff rated disability and dependence as worse than did residents. This probably reflects differences in perspectives. Red amber green (RAG) thresholds of 10 and 5 points are suggested for monitoring changes in care home mean scores.

Measuring Equity Across the Nordic Education Systems—Conceptual and Methodological Choices as Implications for Educational Policies

Ever since international large-scale student assessments made it possible to rank countries according to their equitability, Nordic countries have topped these rankings. Nevertheless, a decline in equity has been reported lately. However, the process of empirical enquiry that leads to specific inferences on equity partly stays obscure to education decision-makers. This unawareness of the boundaries of specific methodological and analytical approaches may lead to wrong interpretations and policy implications. Therefore, our aim is to discuss and empirically illustrate how the array of choices taken throughout the research process, from equity conceptualization and operationalization to its measurement, may affect the inferences on educational equity for Nordic countries. Our sample includes fourth- and eighth-grade students from Norway, Sweden, Denmark and Finland who participated in TIMSS 2015. We applied two-level multigroup regression models within the structural equation modelling framework to investigate the sensitivity of the countries’ level of equity to: (a) operationalization of the socioeconomic status measure; (b) operationalization of equity or, in other words, the method of analysis employed (e.g., bivariate analysis versus univariate); (c) single-level against multilevel analytical approaches; (d) the grade/age of students; and (e) the choice of the learning outcome across subject domains. Prior to the analyses, we estimated the comparability of SES as a latent construct between Nordic countries. Our results confirmed that some of the most common choices to measure educational equity do matter. Thus, we would encourage a researcher to report elaborately on the research process and inform on its limitations because if interpreted wrongly, it may have unfavourable consequences for a particular group of individuals.

Status Characteristics, Implicit Bias, and the Production of Racial Inequality

Racial stratification is well documented in many spheres of social life. Much stratification research assumes that implicit or explicit bias on the part of institutional gatekeepers produces disparate racial outcomes. Research on status-based expectations provides a good starting point for theoretically understanding racial inequalities. In this context it is understood that race results in differential expectations for performance, producing disparate outcomes. But even here, the mechanism (i.e., status-based expectations) is often assumed due to the lack of tools to measure status-based expectations. In this article, we put forth a new way to measure implicit racial status beliefs and theorize how they are related to consensual beliefs about what “most people” think. This enables us to assess the mechanisms in the relationship between race and disparate outcomes. We conducted two studies to assess our arguments. Study 1 demonstrates the measurement properties of the implicit status measure. Study 2 shows how implicit status beliefs and perceptions of what “most people” think combine to shape social influence. We conclude with the implications of this work for social psychological research, and for racial stratification more generally.

Health status of care home residents: practicality and construct validity of data collection by staff at scale

BackgroundMedical diagnoses and assessed need for care are the prerequisites for planning and delivery of care to residents of care homes. Assessing the effectiveness of care is difficult. The aim of this study was to test the practicality and construct validity of the howRu health status measure using secondary analysis of a large data set.MethodThe data came from a Bupa Care Homes Census in 2012, which covered 24 506 residents in 395 homes internationally (UK, Australia and New Zealand). Staff completed optical mark readable forms about each resident using a short generic health status measure, howRu. Response rates were used to assess practicality and expected relationships between health status and independent variables were used to assess the construct validity.Results and discussion19,438 forms were returned (79.3%) in 360 care homes (91.1%); complete health status data were recorded for 18 617 residents (95.8% of those returned). Missing values for any health status items mostly came from a small number of homes. The relationships between howRu and independent variables support construct validity. Factor analysis suggests three latent variables (discomfort, distress and disability/dependence).ConclusionsHowRu proved easy to use and practical at scale. The howRu health status measure shows good construct validity.

Development and initial testing of a Health Confidence Score (HCS)

IntroductionPatients need to feel confident about looking after their own health. This is needed to improve patient outcomes and clinical support. With few suitable tools available to measure self-care health confidence, we developed and validated a short, generic survey instrument for use in evaluation and quality improvement.MethodsThe Health Confidence Score (HCS) was developed through literature review, patient and expert focus groups and discussions. This paper reports an initial survey (n = 1031, study 1) which identified some issues and a further face-to-face survey (n = 378, study 2) to test the construct and concurrent validity of the final version. Scores were correlated against the My Health Confidence (MHC) rating scale, howRu (health status measure) and relevant demographics.ResultsThe HCS is short (50 words) with good readability (reading age 8). It has four items covering health knowledge, capability to self-manage, access to help and shared decision-making; each has four response options (strongly agree, agree, neutral disagree). Items are reported independently and as a summary score.The mean summary score was 76.7 (SD 20.4) on 0–100 scale. Cronbach’s alpha = 0.82. Exploratory factor analysis suggested that the four items relate to a single dimension. Correlation of the HCS summary score with MHC was high (Spearman r = 0.76). It was also associated with health status (Spearman r = 0.49), negatively with number of medications taken (r=–0.29) and age (r=–0.22) and not with ethnicity, having children or education level.ConclusionsThe HCS is short, easy to use, with good psychometric properties and construct validity. Each item is meaningful independently and the summary score gives an overall picture of health confidence.