997 resultados para attitude measurement


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Background: Caring for family members with dementia can be a long-term, burdensome task resulting in physical and emotional distress and impairment. Research has demonstrated significantly lower levels of selfefficacy among family caregivers of people with dementia (CGs) than caregivers of relatives with non-dementia diseases. Intervention studies have also suggested that the mental and physical health of dementia CGs could be improved through the enhancement of their self-efficacy. However, studies are limited in terms of the influences of caregiver self-efficacy on caregiver behaviour, subjective burden and health-related quality of life. Of particular note is that there are no studies on the applicability of caregiver self-efficacy in the social context of China. Objective: The purpose of this thesis was to undertake theoretical exploration using Bandura’s (1997) self-efficacy theory to 1) revise the Revised Caregiving Self-Efficacy Scale (C-RCSES) (Steffen, McKibbin, Zeiss, Gallagher-Thompson, & Bandura, 2002), and 2) explore determinants of caregiver self-efficacy and the role of caregiver self-efficacy and other conceptual constructs (including CGs’ socio-demographic characteristics, CRs’ impairment and CGs’ social support) in explaining and predicting caregiver behaviour, subjective burden and health-related quality of life among CGs in China. Methodology: Two studies were undertaken: a qualitative elicitation study with 10 CGs; and a cross-sectional survey with 196 CGs. In the first study, semi-structured interviews were conducted to explore caregiver behaviours and corresponding challenges for their performance. The findings of the study assisted in the development of the initial items and domains of the Chinese version of the Revised Caregiving Self-Efficacy Scale (C-RCSES). Following changes to items in the scale, the second study, a cross-sectional survey with 196 CGs was conducted to evaluate the psychometric properties of C-RCSES and to test a hypothesised self-efficacy model of family caregiving adapted from Bandura’s theory (1997). Results: 35 items were generated from the qualitative data. The content validity of the C-RCSES was assessed and ensured in Study One before being used for the cross-sectional survey. Eight items were removed and five subscales (caregiver self-efficacy for gathering information about treatment, symptoms and health care; obtaining support; responding to problematic behaviours; management of household, personal and medical care; and controlling upsetting thoughts about caregiving) were identified after principal component factor analysis on the cross-sectional survey data. The reliability of the scale is acceptable: the Cronbach’s alpha coefficients for the whole scale and for each subscale were all over .80; and the fourweek test-retest reliabilities for the whole scale and for each subscale ranged from .64 to .85. The concurrent, convergent and divergent validity were also acceptable. CGs reported moderate levels of caregiver self-efficacy. Furthermore, the level of self-efficacy for management of household, personal and medical care was relatively high in comparison to those of the other four domains of caregiver self-efficacy. Caregiver self-efficacy was also significantly influenced by CGs’ socio-demographic characteristics and the caregiving external factors (CR impairment and social support that CGs obtained). The level of caregiver behaviour that CGs reported was higher than that reported in other Chinese research. CGs’ socio-demographics significantly influenced caregiver behaviour, whereas caregiver self-efficacy did not influence caregiver behaviour. Regarding the two external factors, CGs who cared for highly impaired relatives reported high levels of caregiver behaviour, but social support did not influence caregiver behaviour. Regarding caregiver subjective burden and health-related quality of life, CGs reported moderate levels of subjective burden, and their level of healthrelated quality of life was significantly lower than that of the general population in China. The findings also indicated that CGs’ subjective burden and health-related quality of life were influenced by all major factors in the hypothesised model, including CGs’ socio-demographics, CRs’ impairment, social support that CGs obtained, caregiver self-efficacy and caregiver behaviour. Of these factors, caregiver self-efficacy and social support significantly improved their subjective burden and health-related quality of life; whereas caregiver behaviour and CRs’ impairment were detrimental to CGs, such as increasing subjective burden and worsening health-related quality of life. Conclusion: While requiring further exploration, the qualitative study was the first qualitative research conducted in China to provide an in-depth understanding of CGs’ caregiving experience, including their major caregiver behaviours and the corresponding challenges. Meanwhile, although the C-RCSES needs further psychometric testing, it is a useful tool for assessing caregiver self-efficacy in Chinese populations. Results of the qualitative and quantitative study provide useful information for future studies regarding the explanatory power of caregiver self-efficacy to caregiver behaviour, subjective burden and health-related quality of life. Additionally, integrated with Bandura’s theory, the findings from the quantitative study also suggested a further study exploring the role of outcome expectations in caregiver behaviour, subjective burden and healthrelated quality of life.

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Understanding the relationship between diet, physical activity and health in humans requires accurate measurement of body composition and daily energy expenditure. Stable isotopes provide a means of measuring total body water and daily energy expenditure under free-living conditions. While the use of isotope ratio mass spectrometry (IRMS) for the analysis of 2H (Deuterium) and 18O (Oxygen-18) is well established in the field of human energy metabolism research, numerous questions remain regarding the factors which influence analytical and measurement error using this methodology. This thesis was comprised of four studies with the following emphases. The aim of Study 1 was to determine the analytical and measurement error of the IRMS with regard to sample handling under certain conditions. Study 2 involved the comparison of TEE (Total daily energy expenditure) using two commonly employed equations. Further, saliva and urine samples, collected at different times, were used to determine if clinically significant differences would occur. Study 3 was undertaken to determine the appropriate collection times for TBW estimates and derived body composition values. Finally, Study 4, a single case study to investigate if TEE measures are affected when the human condition changes due to altered exercise and water intake. The aim of Study 1 was to validate laboratory approaches to measure isotopic enrichment to ensure accurate (to international standards), precise (reproducibility of three replicate samples) and linear (isotope ratio was constant over the expected concentration range) results. This established the machine variability for the IRMS equipment in use at Queensland University for both TBW and TEE. Using either 0.4mL or 0.5mL sample volumes for both oxygen-18 and deuterium were statistically acceptable (p>0.05) and showed a within analytical variance of 5.8 Delta VSOW units for deuterium, 0.41 Delta VSOW units for oxygen-18. This variance was used as “within analytical noise” to determine sample deviations. It was also found that there was no influence of equilibration time on oxygen-18 or deuterium values when comparing the minimum (oxygen-18: 24hr; deuterium: 3 days) and maximum (oxygen-18: and deuterium: 14 days) equilibration times. With regard to preparation using the vacuum line, any order of preparation is suitable as the TEE values fall within 8% of each other regardless of preparation order. An 8% variation is acceptable for the TEE values due to biological and technical errors (Schoeller, 1988). However, for the automated line, deuterium must be assessed first followed by oxygen-18 as the automated machine line does not evacuate tubes but merely refills them with an injection of gas for a predetermined time. Any fractionation (which may occur for both isotopes), would cause a slight elevation in the values and hence a lower TEE. The purpose of the second and third study was to investigate the use of IRMS to measure the TEE and TBW of and to validate the current IRMS practices in use with regard to sample collection times of urine and saliva, the use of two TEE equations from different research centers and the body composition values derived from these TEE and TBW values. Following the collection of a fasting baseline urine and saliva sample, 10 people (8 women, 2 men) were dosed with a doubly labeled water does comprised of 1.25g 10% oxygen-18 and 0.1 g 100% deuterium/kg body weight. The samples were collected hourly for 12 hrs on the first day and then morning, midday, and evening samples were collected for the next 14 days. The samples were analyzed using an isotope ratio mass spectrometer. For the TBW, time to equilibration was determined using three commonly employed data analysis approaches. Isotopic equilibration was reached in 90% of the sample by hour 6, and in 100% of the sample by hour 7. With regard to the TBW estimations, the optimal time for urine collection was found to be between hours 4 and 10 as to where there was no significant difference between values. In contrast, statistically significant differences in TBW estimations were found between hours 1-3 and from 11-12 when compared with hours 4-10. Most of the individuals in this study were in equilibrium after 7 hours. The TEE equations of Prof Dale Scholler (Chicago, USA, IAEA) and Prof K.Westerterp were compared with that of Prof. Andrew Coward (Dunn Nutrition Centre). When comparing values derived from samples collected in the morning and evening there was no effect of time or equation on resulting TEE values. The fourth study was a pilot study (n=1) to test the variability in TEE as a result of manipulations in fluid consumption and level of physical activity; the magnitude of change which may be expected in a sedentary adult. Physical activity levels were manipulated by increasing the number of steps per day to mimic the increases that may result when a sedentary individual commences an activity program. The study was comprised of three sub-studies completed on the same individual over a period of 8 months. There were no significant changes in TBW across all studies, even though the elimination rates changed with the supplemented water intake and additional physical activity. The extra activity may not have sufficiently strenuous enough and the water intake high enough to cause a significant change in the TBW and hence the CO2 production and TEE values. The TEE values measured show good agreement based on the estimated values calculated on an RMR of 1455 kcal/day, a DIT of 10% of TEE and activity based on measured steps. The covariance values tracked when plotting the residuals were found to be representative of “well-behaved” data and are indicative of the analytical accuracy. The ratio and product plots were found to reflect the water turnover and CO2 production and thus could, with further investigation, be employed to identify the changes in physical activity.

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In this paper, a plasmonic “ac Wheatstone bridge” circuit is proposed and theoretically modeled for the first time. The bridge circuit consists of three metallic nanoparticles, shaped as rectangular prisms, with two nanoparticles acting as parallel arms of a resonant circuit and the third bridging nanoparticle acting as an optical antenna providing an output signal. Polarized light excites localized surface plasmon resonances in the two arms of the circuit, which generate an optical signal dependent on the phase-sensitive excitations of surface plasmons in the antenna. The circuit is analyzed using a plasmonic coupling theory and numerical simulations. The analyses show that the plasmonic circuit is sensitive to phase shifts between the arms of the bridge and has the potential to detect the presence of single molecules.

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The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

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The concept of system use has suffered from a "too simplistic definition" (DeLone and McLean [9], p. 16). This paper reviews various attempts at conceptualization and measurement of system use and then proposes a re-conceptualization of it as "the level of incorporation of an information system within a user's processes." We then go on to develop the concept of a Functional Interface Point and four dimensions of system usage: automation level, the proportion of the business process encoded by the information system; extent, the proportion of the FIPs used by the business process; frequency, the rate at which FIPs are used by the participants in the process; and thoroughness, the level of use of information/functionality provided by the system at an FIP. The article concludes with a discussion of some implications of this re-conceptualization and areas for follow on research.

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Organizations invest heavily in Customer Relationship Management (CRM) and Supply Chain Management (SCM) systems, and their related infrastructure, presumably expecting positive benefits to the organization. Assessing the benefits of such systems is an important aspect of managing such systems. Given the substantial differences between CRM and SCM systems with traditional intra-organizational applications, existing Information Systems benefits measurement models and frameworks are ill-suited to gauge CRM and SCM benefits. This paper reports the preliminary findings of a research that seeks to develop a measurement model to assess benefits of CRM and SCM applications. The a-priori benefits measurement model is developed reviewing the 55 academic studies and 40 practitioner papers. The review of related literature yielded 606 benefits, which were later synthesized into 74 mutually exclusive benefit measures of CRM and SCM applications arranged under five dimensions.

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Background: Chronic disease presents overwhelming challenges to elderly patients, their families, health care providers and the health care system. The aim of this study was to explore a theoretical model for effective management of chronic diseases, especially type 2 diabetes mellitus and/or cardiovascular disease. The assumed theoretical model considered the connections between physical function, mental health, social support and health behaviours. The study effort was to improve the quality of life for people with chronic diseases, especially type 2 diabetes and/or cardiovascular disease and to reduce health costs. Methods: A cross-sectional post questionnaire survey was conducted in early 2009 from a randomised sample of Australians aged 50 to 80 years. A total of 732 subjects were eligible for analysis. Firstly, factors influencing respondents‘ quality of life were investigated through bivariate and multivariate regression analysis. Secondly, the Theory of Planned Behaviour (TPB) model for regular physical activity, healthy eating and medication adherence behaviours was tested for all relevant respondents using regression analysis. Thirdly, TPB variable differences between respondents who have diabetes and/or cardiovascular disease and those without these diseases were compared. Finally, the TPB model for three behaviours including regular physical activity, healthy eating and medication adherence were tested in respondents with diabetes and/or cardiovascular diseases using Structure Equation Modelling (SEM). Results: This was the first study combining the three behaviours using a TPB model, while testing the influence of extra variables on the TPB model in one study. The results of this study provided evidence that the ageing process was a cumulative effect of biological change, socio-economic environment and lifelong behaviours. Health behaviours, especially physical activity and healthy eating were important modifiable factors influencing respondents‘ quality of life. Since over 80% of the respondents had at least one chronic disease, it was important to consider supporting older people‘s chronic disease self-management skills such as healthy diet, regular physical activity and medication adherence to improve their quality of life. Direct measurement of the TPB model was helpful in understanding respondents‘ intention and behaviour toward physical activity, healthy eating and medication adherence. In respondents with diabetes and/or cardiovascular disease, the TPB model predicted different proportions of intention toward three different health behaviours with 39% intending to engage in physical activity, 49% intending to engage in healthy eating and 47% intending to comply with medication adherence. Perceived behavioural control, which was proven to be the same as self-efficacy in measurement in this study, played an important role in predicting intention towards the three health behaviours. Also social norms played a slightly more important role than attitude for physical activity and medication adherence, while attitude and social norms had similar effects on healthy eating in respondents with diabetes and/or cardiovascular disease. Both perceived behavioural control and intention directly predicted recent actual behaviours. Physical activity was more a volitional control behaviour than healthy eating and medication adherence. Step by step goal setting and motivation was more important for physical activity, while accessibility, resources and other social environmental factors were necessary for improving healthy eating and medication adherence. The extra variables of age, waist circumference, health related quality of life and depression indirectly influenced intention towards the three behaviours mainly mediated through attitude and perceived behavioural control. Depression was a serious health problem that reduced the three health behaviours‘ motivation, mediated through decreased self-efficacy and negative attitude. This research provided evidence that self-efficacy is similar to perceived behavioural control in the TPB model and intention is a proximal goal toward a particular behaviour. Combining four sources of information in the self-efficacy model with the TPB model would improve chronic disease patients‘ self management behaviour and reach an improved long-term treatment outcome. Conclusion: Health intervention programs that target chronic disease management should focus on patients‘ self-efficacy. A holistic approach which is patient-centred and involves a multidisciplinary collaboration strategy would be effective. Supporting the socio-economic environment and the mental/ emotional environment for older people needs to be considered within an integrated health care system.

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Background: To compare the intraocular pressure readings obtained with the iCare rebound tonometer and the 7CR non-contact tonometer with those measured by Goldmann applanation tonometry in treated glaucoma patients. Design: A prospective, cross sectional study was conducted in a private tertiary glaucoma clinic. Participants: 109 (54M:55F) patients including only eyes under medical treatment for glaucoma. Methods: Measurement by Goldmann applanation tonometry, iCare rebound tonometry and 7CR non-contact tonometry. Main Outcome Measures: Intraocular pressure. Results: There were strong correlations between the intraocular pressure measurements obtained with Goldmann and both the rebound and non-contact tonometers (Spearman r values ≥ 0.79, p < 0.001). However, there were small, statistically significant differences between the average readings for each tonometer. For the rebound tonometer, the mean intraocular pressure was slightly higher compared to the Goldmann applanation tonometer in the right eyes (p = 0.02), and similar in the left eyes (p = 0.93) however these differences did not reach statistical significance. The Goldmann correlated measurements from the noncontact tonometer were lower than the average Goldmann reading for both right (p < 0.001) and left (p > 0.01) eyes. The corneal compensated measurements from the non-contact tonometer were significantly higher compared to the other tonometers (p ≤ 0.001). Conclusions: The iCare rebound tonometer and the 7CR non-contact tonometer measure IOP in fundamentally different ways to the Goldmann applanation tonometer. The resulting IOP values vary between the instruments and will need to be considered when comparing clinical versus home acquired measurements.