Dataset Information

How does ageism influence frailty? A preliminary study using a structural equation model.

ABSTRACT: BACKGROUND:Based on the Stereotype Embodiment Theory (SET), this study aims to examine the mechanism of ageism on frailty through the proposed framework of "Experiences of Ageism (EA) ? Age Stereotypes (AS) ? Attitudes to Ageing (AA) ? Frailty" using a structural equation model (SEM). METHODS:A community-based study involving 630 participants aged 60?years and older was conducted in Shanghai. EA, AS, AA and frailty status were assessed by validated scales. In particular, EA included three parts in this study, as the first part was the experiences of explicit prejudice or discrimination because of age, another two parts were the experiences of witnessed and encountered implicit negative age-based stereotypes. A SEM was performed to examine whether the proposed paths from EA to frailty were supported. RESULTS:EA had a significant indirect effect (?'?=?.360*-.456*-.576?=?.095, p?

SUBMITTER: Ye B

PROVIDER: S-EPMC7586685 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

How does ageism influence frailty? A preliminary study using a structural equation model.

Ye Bo B Gao Junling J Fu Hua H Chen Hao H Dong Wenjing W Gu Ming M

BMC geriatrics 20201026 1

<h4>Background</h4>Based on the Stereotype Embodiment Theory (SET), this study aims to examine the mechanism of ageism on frailty through the proposed framework of "Experiences of Ageism (EA) → Age Stereotypes (AS) → Attitudes to Ageing (AA) → Frailty" using a structural equation model (SEM).<h4>Methods</h4>A community-based study involving 630 participants aged 60 years and older was conducted in Shanghai. EA, AS, AA and frailty status were assessed by validated scales. In particular, EA includ ...[more]

PMID: 33106186

Similar Datasets

Project description:BACKGROUND:Children's oral health status (OHS) can be influenced by their oral health practices and many parental factors. This study aimed to investigate pathways from parental factors to oral health practices and status of children in Hong Kong. METHODS:Using a combination of random and purposive sampling of Hong Kong households, 432 families with children aged 5-7 participated in a cross-sectional survey. Data on socioeconomic status, smoking, and oral health knowledge, attitudes, and practices, as well as OHS of parents and parents' knowledge of and attitudes towards their children's oral health, were collected through a questionnaire. Tooth status, periodontal status, and oral hygiene data were also collected through clinical examination. Correlations of oral health behaviors (OHB) and OHS within families were assessed by confirmatory factor analysis. A conceptual model of the parental influences on children's oral health practices and status was tested by a structural equation model (SEM). Chi-square test, chi-square/df, nonnormed fit index, comparative fit index, and root mean square error of approximation were used to assess the model fit. RESULTS:Fit indexes for confirmatory factor analysis and SEM showed good fit. Positive correlations of OHB and OHS were found within the families that ranged from 0.74 to 0.98 for OHB and 0.30 to 0.43 for OHS. SEM showed better socioeconomic status of mothers led to better oral health knowledge and attitude (γ = 0.75, P < 0.001) and also towards their children's better oral health knowledge and attitude (γ = 0.44, P < 0.01). Parents' attitudes towards their children's oral health (β = 0.40, P = 0.04) and mothers' OHB (β = 0.60, P < 0.001) were positively associated with OHB of children. Positive OHB of children (β = - 0.48, P < 0.01) in turn led to better oral health. CONCLUSIONS:Correlations of OHB and OHS between mothers and children were stronger than those of fathers. Children's OHS was directly affected by their mothers' OHB, which in turn were affected by parents' oral health knowledge, attitudes, and practices.

Project description:The contamination of fresh produce with foodborne pathogens has been an on-going concern with outbreaks linked to these commodities. Evaluation of farm practices, such as use of manure, irrigation water source, and other factors that could influence pathogen prevalence in the farming environment could lead to improved mitigation strategies to reduce the potential for contamination events. Soil, water, manure, and compost were sampled from farms in Ohio and Georgia to identify the prevalence of Salmonella, Listeria monocytogenes (Lm), Campylobacter, and Shiga-toxin-producing Escherichia coli (STEC), as well as Arcobacter, an emerging human pathogen. This study investigated agricultural practices to determine which influenced pathogen prevalence, i.e., the percent positive samples. These efforts identified a low prevalence of Salmonella, STEC, and Campylobacter in soil and water (< 10%), preventing statistical modeling of these pathogens. However, Lm and Arcobacter were found in soil (13 and 7%, respectively), manure (49 and 32%, respectively), and water samples (18 and 39%, respectively) at a comparatively higher prevalence, suggesting different dynamics are involved in their survival in the farm environment. Lm and Arcobacter prevalence data, soil chemical characteristics, as well as farm practices and weather, were analyzed using structural equation modeling to identify which factors play a role, directly or indirectly, on the prevalence of these pathogens. These analyses identified an association between pathogen prevalence and weather, as well as biological soil amendments of animal origin. Increasing air temperature increased Arcobacter and decreased Lm. Lm prevalence was found to be inversely correlated with the use of surface water for irrigation, despite a high Lm prevalence in surface water suggesting other factors may play a role. Furthermore, Lm prevalence increased when the microbiome's Simpson's Diversity Index decreased, which occurred as soil fertility increased, leading to an indirect positive effect for soil fertility on Lm prevalence. These results suggest that pathogen, environment, and farm management practices, in addition to produce commodities, all need to be considered when developing mitigation strategies. The prevalence of Arcobacter and Lm versus the other pathogens suggests that multiple mitigation strategies may need to be employed to control these pathogens.

Project description:IntroductionSurgical procedures are increasingly carried out in a day-case setting. Along with this increase, psychological outcomes have become prominent. The objective was to evaluate prospectively the prognostic effects of sociodemographic, medical, and psychological variables assessed before day-case surgery on psychological outcomes after surgery.MethodsThe study was carried out between October 2010 and September 2011. We analyzed 398 mixed patients, from a randomized controlled trial, undergoing day-case surgery at a university medical center. Structural equation modeling was used to jointly study presurgical prognostic variables relating to sociodemographics (age, sex, nationality, marital status, having children, religion, educational level, employment), medical status (BMI, heart rate), and psychological status associated with anxiety (State-Trait Anxiety Inventory (STAI), Hospital Anxiety and Depression Scale (HADS-A)), fatigue (Multidimensional Fatigue Inventory (MFI)), aggression (State-Trait Anger Scale (STAS)), depressive moods (HADS-D), self-esteem, and self-efficacy. We studied psychological outcomes on day 7 after surgery, including anxiety, fatigue, depressive moods, and aggression regulation.ResultsThe final prognostic model comprised the following variables: anxiety (STAI, HADS-A), fatigue (MFI), depression (HADS-D), aggression (STAS), self-efficacy, sex, and having children. The corresponding psychological variables as assessed at baseline were prominent (i.e. standardized regression coefficients ≥ 0.20), with STAI-Trait score being the strongest predictor overall. STAI-State (adjusted R2 = 0.44), STAI-Trait (0.66), HADS-A (0.45) and STAS-Trait (0.54) were best predicted.ConclusionWe provide a prognostic model that adequately predicts multiple postoperative outcomes in day-case surgery. Consequently, this enables timely identification of vulnerable patients who may require additional medical or psychological preventive treatment or-in a worst-case scenario-could be unselected for day-case surgery.

Dataset Information

How does ageism influence frailty? A preliminary study using a structural equation model.

Publications

How does ageism influence frailty? A preliminary study using a structural equation model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets