Dataset Information

Should schools expect poor physical and mental health, social adjustment, and participation outcomes in students with disability?

ABSTRACT: The literature on whether students with disabilities have worse physical and mental health, social adjustment, and participation outcomes when compared to their peers without disabilities is largely inconclusive. While the majority of case control studies showed significantly worse outcomes for students with disabilities; the proportion of variance accounted for is rarely reported. The current study used a population cross-sectional approach to determine the classification ability of commonly used screening and outcome measures in determining the disability status. Furthermore, the study aimed to identify the variables, if any, that best predicted the presence of disability. Results of univariate discriminant function analyses suggest that across the board, the sensitivity of the outcome/screening tools to correctly identify students with a disability was 31.9% higher than the related Positive Predictive Value (PPV). The lower PPV and Positive Likelihood Ratio (LR+) scores suggest that the included measures had limited discriminant ability (17.6% to 40.3%) in accurately identifying students at-risk for further assessment. Results of multivariate analyses suggested that poor health and hyperactivity increased the odds of having a disability about two to three times, while poor close perceived friendship and academic competences predicted disability with roughly the same magnitude. Overall, the findings of the current study highlight the need for researchers and clinicians to familiarize themselves with the psychometric properties of measures, and be cautious in matching the function of the measures with their research and clinical needs.

SUBMITTER: Vaz S

PROVIDER: S-EPMC4429077 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe co-occurrence of mental and physical chronic conditions (mental-physical multimorbidity) is a growing and largely unaddressed challenge for health systems and wider economies in low-and middle-income countries. This study investigated the independent and combined (additive or synergistic) effects of mental and physical chronic conditions on disability, work productivity, and social participation in China.MethodsPanel data study design utilised two waves of the China Health and Retirement Longitudinal Study (2011, 2015), including 5616 participants aged ≥45 years, 12 physical chronic conditions and depression. We used a panel data approach of random-effects regression models to assess the relationships between mental-physical multimorbidity and outcomes.ResultsAfter adjusting for socio-economic and demographic factors, an increased number of physical chronic conditions was independently associated with a higher likelihood of disability (Adjusted odds ratio (AOR) = 1.39; 95% CI: 1.33, 1.45), early retirement (AOR = 1.37 [1.26, 1.49]) and increased sick leave days (1.25 days [1.16, 1.35]). Depression was independently associated with disability (AOR = 3.78 [3.30, 4.34]), increased sick leave days (2.18 days [1.72, 2.77]) and a lower likelihood of social participation (AOR = 0.57 [0.47, 0.70]), but not with early retirement (AOR = 1.24 [0.97, 1.58]). There were small and statistically insignificant interactions between physical chronic conditions and mental health on disability, work productivity and social participation, suggesting an additive effect of mental-physical multimorbidity on productivity loss.ConclusionMental-physical multimorbidity poses substantial negative health and economic effects on individuals, health systems, and societies. More research that addresses the challenges of mental-physical multimorbidity is needed to inform the development of interventions that can be applied to the workplace and the wider community in China.

Dataset Information

Should schools expect poor physical and mental health, social adjustment, and participation outcomes in students with disability?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets