410 resultados para Demographic Inferences
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Background: Quality of work life (QWL) is defined as the extent to which employee is satisfied with personal and working needs through participating in the workplace while achieving the organisation’s goals. QWL has been found to influence the commitment and productivity of employees in healthcare organisations, as well as in other industries. However, reliable information on the QWL of PHC nurses is limited. The purpose of this study was to assess the QWL among PHC nurses in the Jazan region, Saudi Arabia. Methods: A descriptive research design, namely, a cross-sectional survey was used in this study. Data were collected using Brooks’ survey of quality of nursing work life (QNWL) and demographic questions. A convenience sample was recruited from 143 PHC centres in Jazan, Saudi Arabia. The Jazan region is located in the southern part of Saudi Arabia. A response rate of 91% (N = 532/585) was achieved (effective RR = 87%, n = 508). Data analysis consisted of descriptive statistics, t-test and one way-analysis of variance. Total scores and sub-scores for QWL Items and item summary statistics were computed and reported, using SPSS version 17 for Windows. Results: Findings suggested that the respondents were dissatisfied with their work life. The major influencing factors were unsuitable working hours/shifts, lack of facilities for nurses, inability to balance work with family needs, inadequacy of family-leave time, poor staffing, management and supervision practices, lack of professional development opportunities, and inappropriate working environment in terms of the level of security, patient care supplies and equipment, and recreation facilities (Break-area). Other essential factors include the community’s view of nursing and inadequate salary. More positively, the majority of nurses were satisfied with their co-workers, satisfied to be nurses and had a sense of belonging in their workplaces. Significant differences were found according to gender, age, marital status, dependent children, dependent adults, nationality, ethnicity, nursing tenure, organisational tenure, positional tenure, and payment per month. No significant differences were found according to education level and location of PHC. Conclusions: These findings can be used by PHC managers and policy makers for developing and appropriately implementing successful plans to improve the QWL. This will help to enhance the home and work environments, improve individual and organisation performance and increase nurses’ commitment.
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Background Quality of work life (QWL) has been found to influence the commitment of health professionals including nurses. However, reliable information on the QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. Methods A cross-sectional survey was used in this study. Data were collected using Brooks’ survey of Quality of Nursing Work life (QNWL), the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan region, Saudi Arabia completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression (SMR), and hierarchical multiple regression (HMR) were applied for analysis using SPSS v17 for Windows. Results Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using SMR, 26% of the variance in turnover intention was explained by the QWL, p < 0.001, with R² = .263. Further analysis using HMR found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Conclusions Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes.
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Background: Falciparum malaria is the most deadly among the four main types of human malaria. Although great success has been achieved since the launch of the National Malaria Control Programme in 1955, malaria remains a serious public health problem in China. This paper aimed to analyse the geographic distribution, demographic patterns and time trends of falciparum malaria in China. Methods: The annual numbers of falciparum malaria cases during 1992–2003 and the individual case reports of each clinical falciparum malaria during 2004–2005 were extracted from communicable disease information systems in China Center for Diseases Control and Prevention. The annual number of cases and the annual incidence were mapped by matching them to corresponding province- and county-level administrative units in a geographic information system. The distribution of falciparum malaria by age, gender and origin of infection was analysed. Time-series analysis was conducted to investigate the relationship between the falciparum malaria in the endemic provinces and the imported falciparum malaria in non-endemic provinces. Results: Falciparum malaria was endemic in two provinces of China during 2004–05. Imported malaria was reported in 26 non-endemic provinces. Annual incidence of falciparum malaria was mapped at county level in the two endemic provinces of China: Yunnan and Hainan. The sex ratio (male vs. female) for the number of cases in Yunnan was 1.6 in the children of 0–15 years and it reached 5.7 in the adults over 15 years of age. The number of malaria cases in Yunnan was positively correlated with the imported malaria of concurrent months in the non-endemic provinces. Conclusion: The endemic area of falciparum malaria in China has remained restricted to two provinces, Yunnan and Hainan. Stable transmission occurs in the bordering region of Yunnan and the hilly-forested south of Hainan. The age and gender distribution in the endemic area is characterized by the predominance of adult men cases. Imported falciparum malaria in the non-endemic area of China, affected mainly by the malaria transmission in Yunnan, has increased both spatially and temporally. Specific intervention measures targeted at the mobile population groups are warranted.
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Objective: To develop a physical activity directory (PAD) for Brisbane people over the age of 50 years for distribution by two methods (given or requested), and to determine its effectiveness in raising awareness and encouraging older people to participate in local physical activity options. Methods: Baseline demographic data and stage of change was collected from 224 participants who received the directory. Participants were interviewed by telephone six weeks later to determine their use of the directory on a number of dimensions. Results: Most participants interviewed at follow-up remembered reading the directory. Participants who requested the directory were significantly more likely than those who were given it to: be contemplators, read the directory, plan to ring a number, plan to attend a class, and to share the directory with others. Participants who were contemplators were significantly more likely to have participated in physical activity of their own and rang a number from the directory. The directory increased over half the participants' awareness of local physical activity options, yet only 7% reported ringing a number and 15% reported doing their own physical activity. Conclusions: The directory was more effective in raising awareness about physical activity options than encouraging people to participate in physical activity, and participants with short-term plans to be more active were more likely to have used the directory. Implications: The directory, even when linked with other services, raises awareness about physical activity options, but has minimal short-term influence on participation.
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Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.
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Background: Effective self-management of diabetes is essential for the reduction of diabetes-related complications, as global rates of diabetes escalate. Methods: Randomised controlled trial. Adults with type 2 diabetes (n = 120), with HbA1c greater than or equal to 7.5 %, were randomly allocated (4 × 4 block randomised block design) to receive an automated, interactive telephone-delivered management intervention or usual routine care. Baseline sociodemographic, behavioural and medical history data were collected by self-administered questionnaires and biological data were obtained during hospital appointments. Health-related quality of life (HRQL) was measured using the SF-36. Results: The mean age of participants was 57.4 (SD 8.3), 63 % of whom were male. There were no differences in demographic, socioeconomic and behavioural variables between the study arms at baseline. Over the six-month period from baseline, participants receiving the Australian TLC (Telephone-Linked Care) Diabetes program showed a 0.8 % decrease in geometric mean HbA1c from 8.7 % to 7.9 %, compared with a 0.2 % HbA1c reduction (8.9 % to 8.7 %) in the usual care arm (p = 0.002). There was also a significant improvement in mental HRQL, with a mean increase of 1.9 in the intervention arm, while the usual care arm decreased by 0.8 (p = 0.007). No significant improvements in physical HRQL were observed. Conclusions: These analyses indicate the efficacy of the Australian TLC Diabetes program with clinically significant post-intervention improvements in both glycaemic control and mental HRQL. These observed improvements, if supported and maintained by an ongoing program such as this, could significantly reduce diabetes-related complications in the longer term. Given the accessibility and feasibility of this kind of program, it has strong potential for providing effective, ongoing support to many individuals with diabetes in the future.
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Objective: Food insecurity is the limited or uncertain availability or access to nutritionally-adequate, culturally-appropriate and safe foods. Food insecurity may result in inadequate dietary intakes, overweight or obesity and the development of chronic disease. Internationally, few studies have focused on the range of potential health outcomes related to food insecurity among adults residing in disadvantaged locations and no such Australian studies exist. The objective of this study was to investigate associations between food insecurity, socio-demographic and health factors and dietary intakes among adults residing in disadvantaged urban areas. Design: Data were collected by mail survey (n= 505, 53% response rate), which ascertained information about food security status, demographic characteristics (such as age, gender, household income, education) fruit and vegetable intakes, take-away and meat consumption, general health, depression and chronic disease. Setting: Disadvantaged suburbs of Brisbane city, Australia, 2009. Subjects: Individuals aged ≥ 20 years. Results: Approximately one-in-four households (25%) were food insecure. Food insecurity was associated with lower household income, poorer general health, increased healthcare utilisation and depression. These associations remained after adjustment for age, gender and household income. Conclusion: Food insecurity is prevalent in urbanised disadvantaged areas in developed countries such as Australia. Low-income households are at high risk of experiencing food insecurity. Food insecurity may result in significant health burdens among the population, and this may be concentrated in socioeconomically-disadvantaged suburbs.
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Purpose: Food insecurity is the limited/uncertain availability or ability to acquire nutritionally-adequate, culturally-relevant and safe foods. Adults suffering from food insecurity are at risk of inadequate nutrient intakes or, paradoxically, overweight/obesity and the development of chronic disease. Despite the global financial crisis and rising costs of living, few studies have investigated the potential dietary and health consequences of food insecurity among the Australian population. This study examined whether food insecurity was associated with health behaviours and dietary intakes among adults residing in socioeconomically-disadvantaged urbanised areas. Methods: In this cross-sectional study, a random sample of residents (n = 1000) were selected from the most disadvantaged suburbs of Brisbane city (response rate 51%). Data were collected by postal questionnaire which ascertained information on socio-demographic information, household food security, height, weight, frequency of healthcare utilisation, presence of chronic disease and intakes of fruit, vegetables and take-away. Data were analysed using logistic regression. Results/Findings: The prevalence of food insecurity was 25%. Those reporting food insecurity were two-to-three times more likely to have seen a general practitioner or been hospitalised within the previous 6 months. Furthermore, food insecurity was associated with a three-to-six-fold increase in the likelihood of experiencing depression. Food insecurity was associated with higher intakes of some take-away foods, however was not significantly associated with weight status or intakes of fruits or vegetables among this disadvantaged sample. Conclusion: Food insecurity has potential adverse health consequences that may result in significant health burdens among the population, and this may be concentrated in socioeconomically-disadvantaged suburbs.
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Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.
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Objective: Food insecurity may be associated with a number of adverse health and social outcomes however our knowledge of its public health significance in Australia has been limited by use of a single-item measure in the Australian National Health Surveys (NHS) and, more recently, the exclusion of food security items from these surveys. The current study compares prevalence estimates of food insecurity in disadvantaged urban areas of Brisbane using the one-item NHS measure with three adaptations of the United States Department of Agriculture Food Security Survey Module (USDA-FSSM). Design: Data were collected by postal survey (n= 505, 53% response). Food security status was ascertained by the measure used in the NHS, and the 6-, 10- and 18-item versions of the USDA-FSSM. Demographic characteristics of the sample, prevalence estimates of food insecurity and different levels of food insecurity estimated by each tool were determined. Setting: Disadvantaged suburbs of Brisbane city, Australia, 2009. Subjects: Individuals aged ≥ 18 years. Results: Food insecurity was prevalent in socioeconomically-disadvantaged urban areas, estimated as 19.5% using the single-item NHS measure. This was significantly less than the 24.6% (P <0.01), 22.0% (P = 0.01) and 21.3% (P = 0.03) identified using the 18-item, 10-item and 6-item versions of the USDA-FSSM, respectively. The proportion of the sample reporting more severe levels of food insecurity were 10.7%, 10% and 8.6% for the 18-, 10- and 6-item USDA measures respectively, however this degree of food insecurity could not be ascertained using the NHS measure. Conclusions: The measure of food insecurity employed in the NHS may underestimate its prevalence and public health significance. Future monitoring and surveillance efforts should seek to employ a more accurate measure.
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Background Heart failure (HF) remains a condition with high morbidity and mortality. We tested a telephone support strategy to reduce major events in rural and remote Australians with HF, who have limited healthcare access. Telephone support comprised an interactive telecommunication software tool (TeleWatch) with follow-up by trained cardiac nurses. Methods Patients with a general practice (GP) diagnosis of HF were randomised to usual care (UC) or UC and telephone support intervention (UC+I) using a cluster design involving 143 GPs throughout Australia. Patients were followed for 12 months. The primary end-point was the Packer clinical composite score. Secondary end-points included hospitalisation for any cause, death or hospitalisation, as well as HF hospitalisation. Results Four hundred and five patients were randomised into CHAT. Patients were well matched at baseline for key demographic variables. The primary end-point of the Packer Score was not different between the two groups (P=0.98), although more patients improved with UC+I. There were fewer patients hospitalised for any cause (74 versus 114, adjusted HR 0.67 [95% CI 0.50-0.89], p=0.006) and who died or were hospitalised (89 versus 124, adjusted HR 0.70 [95% CI 0.53 – 0.92], p=0.011), in the UC+I vs UC group. HF hospitalisations were reduced with UC+I (23 versus 35, adjusted HR 0.81 [95% CI 0.44 – 1.38]), although this was not significant (p=0.43). There were 16 deaths in the UC group and 17 in the UC+I group (p=0.43). Conclusions Although no difference was observed in the primary end-point of CHAT (Packer composite score), UC+I significantly reduced the number of HF patients hospitalised amongst a rural and remote cohort. These data suggest that telephone support may be an efficacious approach to improve clinical outcomes in rural and remote HF patients.
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BACKGROUND/OBJECTIVES: To describe the diet quality of a national sample of Australian women with a recent history of gestational diabetes mellitus (GDM) and determine factors associated with adherence to national dietary recommendations. SUBJECTS/METHODS: A postpartum lifestyle survey with 1499 Australian women diagnosed with GDM p3 years previously. Diet quality was measured using the Australian recommended food score (ARFS) and weighted by demographic and diabetes management characteristics. Multinominal logistic regression analysis was used to determine the association between diet quality and demographic characteristics, health seeking behaviours and diabetes-related risk factors. RESULTS: Mean (±s.d.) ARFS was 30.9±8.1 from a possible maximum score of 74. Subscale component scores demonstrated that the nuts/legumes, grains and fruits were the most poorly scored. Factors associated with being in the highest compared with the lowest ARFS quintile included age (odds ratio (OR) 5-year increase=1.40; 95% (confidence interval) CI:1.16–1.68), tertiary education (OR=2.19; 95% CI:1.52–3.17), speaking only English (OR=1.92; 95% CI:1.19–3.08), being sufficiently physically active (OR=2.11; 95% CI:1.46–3.05), returning for postpartum blood glucose testing (OR=1.75; 95% CI:1.23–2.50) and receiving riskreduction advice from a health professional (OR=1.80; 95% CI:1.24–2.60). CONCLUSIONS: Despite an increased risk of type 2 diabetes, women in this study had an overall poor diet quality as measured by the ARFS. Women with GDM should be targeted for interventions aimed at achieving a postpartum diet consistent with the guidelines for chronic disease prevention. Encouraging women to return for follow-up and providing risk reduction advice may be positive initial steps to improve diet quality, but additional strategies need to be identified.
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With the increasing diversity of students attending university, there is a growing interest in the factors predicting academic performance. This study is a prospective investigation of the academic, psychosocial, cognitive, and demographic predictors of academic performance of first year Australian university students. Questionnaires were distributed to 197 first year students 4 to 8 weeks prior to the end of semester exams and overall grade point averages were collected at semester completion. Previous academic performance was identified as the most significant predictor of university performance. Integration into university, self efficacy, and employment responsibilities were also predictive of university grades. Identifying the factors that influence academic performance can improve the targeting of interventions and support services for students at risk of academic problems.
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Background Continued aging of the population is expected to be accompanied by substantial increases in the number of people with dementia and in the number of health care staff required to care for them. Adequate knowledge about dementia among health care staff is important to the quality of care delivered to this vulnerable population. The purpose of this study was to assess knowledge about dementia across a range of health care staff in a regional health service district. Methods Knowledge levels were investigated via the validated 30-item Alzheimer's Disease Knowledge Scale (ADKS). All health service district staff with e-mail access were invited to participate in an online survey. Knowledge levels were compared across demographic categories, professional groups, and by whether the respondent had any professional or personal experience caring for someone with dementia. The effect of dementia-specific training or education on knowledge level was also evaluated. Results A diverse staff group (N = 360), in terms of age, professional group (nursing, medicine, allied health, support staff) and work setting from a regional health service in Queensland, Australia responded. Overall knowledge about Alzheimer's disease was of a generally moderate level with significant differences being observed by professional group and whether the respondent had any professional or personal experience caring for someone with dementia. Knowledge was lower for some of the specific content domains of the ADKS, especially those that were more medically-oriented, such as 'risk factors' and 'course of the disease.' Knowledge was higher for those who had experienced dementia-specific training, such as attendance at a series of relevant workshops. Conclusions Specific deficits in dementia knowledge were identified among Australian health care staff, and the results suggest dementia-specific training might improve knowledge. As one piece of an overall plan to improve health care delivery to people with dementia, this research supports the role of introducing systematic dementia-specific education or training.
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Abstract Background The quantum increases in home Internet access and available online health information with limited control over information quality highlight the necessity of exploring decision making processes in accessing and using online information, specifically in relation to children who do not make their health decisions. Objectives To understand the processes explaining parents’ decisions to use online health information for child health care. Methods Parents (N = 391) completed an initial questionnaire assessing the theory of planned behaviour constructs of attitude, subjective norm, and perceived behavioural control, as well as perceived risk, group norm, and additional demographic factors. Two months later, 187 parents completed a follow-up questionnaire assessing their decisions to use online information for their child’s health care, specifically to 1) diagnose and/or treat their child’s suspected medical condition/illness and 2) increase understanding about a diagnosis or treatment recommended by a health professional. Results Hierarchical multiple regression showed that, for both behaviours, attitude, subjective norm, perceived behavioural control, (less) perceived risk, group norm, and (non) medical background were the significant predictors of intention. For parents’ use of online child health information, for both behaviours, intention was the sole significant predictor of behaviour. The findings explain 77% of the variance in parents’ intention to treat/diagnose a child health problem and 74% of the variance in their intentions to increase their understanding about child health concerns. Conclusions Understanding parents’ socio-cognitive processes that guide their use of online information for child health care is important given the increase in Internet usage and the sometimes-questionable quality of health information provided online. Findings highlight parents’ thirst for information; there is an urgent need for health professionals to provide parents with evidence-based child health websites in addition to general population education on how to evaluate the quality of online health information.