BackgroundDiabetic patients need high awareness of disease prevention to adopt self-management behaviors in their daily life. Central to this activity is patients’ empowerment. Current study was conducted to assess empowerment score and its related factors among type 2 diabetic patients.MethodA cross-sectional study carried out over a period of nine months during 2010–2011. All patients with a diagnosis of type 2 diabetes including those referring to four hospitals affiliated with Tehran University of Medical Sciences were recruited. A total of 688 diabetic patients were identified who met the inclusion criteria and were all included in the study. Patients’ empowerment was measured by Diabetes Empowerment Scale reflecting three dimensions including managing psychosocial aspect of diabetes, assessing dissatisfaction and readiness to change and Setting and achieving diabetes goal. Collected data was analysed using SPSS software version 11.5.ResultsAs total, 688 were available for analysis, ranging from 37–81 years old with mean of 54.41 years (SD = 8.22). The Mean duration of the disease was approximately 6.67 years (SD = 4.58). Dimensions of ‘managing the psychosocial aspect of diabetes’, ‘assessing dissatisfaction and readiness to change’ and ‘setting and achieving diabetes goal’ were all measured and scored for each patient. The mean score for each domain was 25.75 ± 5.55, 24.78 ± 7.54, 27.63 ± 7.90, respectively. Data analysis revealed a statistically significant reverse relationship between age and ‘assessing dissatisfaction and readiness to change’ and ‘setting and achieving diabetes goal’. In addition, disease duration had a statistically significant reverse relationship with ‘assessing dissatisfaction and readiness to change’.ConclusionPatients with type 2 diabetes have the potential to be empowered to manage their chronic disease if they are actively informed and educated.

BackgroundThe purpose of this study was to assess diabetes distress and its related factors among type 2 diabetic patients to better tailor intervention planning in Isfahan-Iran.MethodsA cross-sectional study was conducted in 2011. Study population was patients with type 2 diabetes referring to Omolbanin, an outpatient diabetic center in Isfahan. 140 diabetic patients met the inclusion criteria and were all included in the study. Patient’s diabetes distress was measured by DDS. A 17-item self-report diabetes distress scale was used with subscales reflecting 5 domains: 1) Emotional burden (5 items), 2) Physician distress (4 items), 3) Regimen distress (5 items) and 4) Interpersonal distress (3 items). The responses to each item were rated between 1 and 6 (1 = not a problem, 2 = a slight problem, 3 = a moderate problem, 4 = somewhat serious problem, 5 = a serious problem, 6 = a very serious problem). The minimum and the maximum of score in the scale were 17 and 114 respectively. Collected data was analyzed by using SPSS software version 11.5.ResultsMean age of participants were 53.23 years (SD = 7.82). 54.3% was female, 97.1% was married, and 57.1% had education lower than diploma. The average score of total diabetes distress was 2.96 ± 0.83. The average score of each domain was (3.40 ± 1.18), (2.57 ± 0.88), (2.97 ± 0.90), (2.76 ± 0.91) respectively. ‘Emotional Burden’ was considered as the most important domain in measuring diabetes distress. Total diabetes distress had significant association with age (p = 0.02), duration of diabetes (p<0.001), marital status, comorbidity, complications (p<0.001), and history of diabetes (p = 0.01). Pearson correlation coefficient revealed that diabetes distress of type 2 diabetic patients has a linear and direct relation with HbAlc (r = 0.63, p<0.001).ConclusionIt seems some keywords have a main role in diabetes distress such as emotional support, communication with patient and physician, self-efficacy and social support. All of these points are achievable through empowerment approach in diabetes care plan.

AimTo explore the association(s) between demographic factors, socioeconomic status (SES), social capital, health-related quality of life (HRQoL), and mental health among residents of Tehran, Iran.MethodsThe pooled data (n = 31 519) were extracted from a population-based survey Urban Health Equity Assessment and Response Tool-2 (Urban HEART-2) conducted in Tehran in 2011. Mental health, social capital, and HRQoL were assessed using the 28-item General Health Questionnaire (GHQ-28), social capital questionnaire, and Short-Form Health Survey (SF-12), respectively. The study used a multistage sampling method. Social capital, HRQoL, and SES were considered as latent variables. The association between these latent variables, demographic factors, and mental health was determined by structural-equation modeling (SEM).ResultsThe mean age and mental health score were 44.48 ± 15.87 years and 23.33 ± 11.10 (range, 0-84), respectively. The prevalence of mental disorders was 41.76% (95% confidence interval 41.21-42.30). The SEM model showed that age was directly associated with social capital (P = 0.016) and mental health (P = 0.001). Sex was indirectly related to mental health through social capital (P = 0.018). SES, HRQoL, and social capital were associated both directly and indirectly with mental health status.ConclusionThis study suggests that changes in social capital and SES can lead to positive changes in mental health status and that individual and contextual determinants influence HRQoL and mental health.

Despite many calls for the utilisation of research evidence in health policy-making, it is not widely practised, and little is known about how decision-makers in healthcare organisations actually make decisions. We recruited a purposive sample of Australian healthcare decision-makers to complete a web-based survey. We then took a sub-sample from willing respondents for individual interviews. All interviews were audio-recorded, transcribed verbatim and coded thematically. We found that resource allocation decision-making varied greatly across the Australian healthcare system. Decision-making was highly dependent on the operational context in time, place and purpose, and that research evidence was rarely exploited to its full potential. Decision-making involved a multifaceted interplay of elements in situation of inquiry. All decisions were made by networks or collectives of people; and no instance of individual decision-making was reported. This varied, social and contextual nature of decision-making points to a complexity that is not reflected in systematic evidence-based reviews or evidence-based models for decision-making, and we did not discover an appropriate model to reflect this complexity in the health- related literature. We developed a model of ‘adaptive decision-making’ that has potential to guide robust decision-making in complex situations, and could have some value as an explanatory or theoretical model for teaching and practice. What is known about the topic? The topic is certainly novel and original, relevant and timely for academics and healthcare decision-makers. Despite increasing calls for the use of systematic evidence-based reviews including economic evaluations, the way in which decision-makers arrive at their allocation decisions and how such decisions reflect concern for economic efficiency is often blurred. This topic is an important one for its relevance to the current difficulties in the complex situation of healthcare. What does this paper add? This paper shows that decision-makers acknowledged the integration of economic principles as contextual realities into their decision-making activities, rather than utilising the results of ever-more seemingly ‘technically sound’ economic evaluations, which cannot address the inherent uncertainty attached to complex decision-making activities. We developed a novel adaptive model of decision-making generated by the interplay of multiple behaviours and factors in the situation of inquiry. The model is new and takes into account the complexity of the context in time, place, purpose and administrative location. What are the implications for practitioners? This paper should be of interest to a broad readership including those interested in health economics, public health policy, healthcare delivery, healthcare resource allocation and decision-making. The adaptive decision-making model designed in this study has the potential as a guide or heuristic device for teaching and practice. Healthcare decision-makers need to be prepared for complexity and ambiguity and cannot expect the data to tell them everything they need to know. We expect to see a shift in the literature on healthcare decision-making, not away from evidence-based practice and economic evaluation, but towards contextualising these methods in broader, adaptive models of decision-making.