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Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

Chronic disease identification and management is a significant issue in Australia, with general practice being the primary contact point for those at risk of, or living with, chronic disease. However, there is a well-described gap between guideline recommendations for chronic disease management and translation in the general practice setting. In 2018, a group of researchers, clinicians and software developers collaborated to develop a tool to support the identification and management of chronic disease in general practice, with the aim to create a platform that met the needs of general practice. The co-design process drew together core principles and expectations for the establishment of a technological platform, called Future Health Today (FHT), which would sit alongside the electronic medical record (EMR) management system within general practice. FHT used algorithms applied to EMR data to identify patients with, or at risk of, chronic disease and requiring review. Using chronic kidney disease as a clinical focus, the FHT prototype was piloted in a large, metropolitan general practice, and a large regional general practice. Based on user feedback, the prototype was further developed and improved. This paper provides a report on the key features and functionalities that participants identified and implemented in practice. Future Health Today is a new platform, co-designed by general practice for general practice, which aims to streamline the identification and management of chronic disease to improve health outcomes. This paper describes the development of the technology platform and how it was optimised through an implementation study in general practice. [ABSTRACT FROM AUTHOR]

Child abuse commonly occurs within the context of multiple risk factors, in particular parental mental health and/or drug and alcohol problems. As no one agency can address all these factors, inter‐agency collaboration is paramount to the protection of vulnerable children, especially in families with a complex array of problems. This paper outlines a range of recommendations to improve collaboration between child protection workers and mental health/drug & alcohol (MH/D&A) clinicians from the perspective of Keep Them Safe‐Whole Family Team (KTS‐WFT) clinicians. Taking referrals from child protection, the KTS‐WFT offers interventions to families with parental MH/D&A problems where there are child protection concerns. As part of a larger evaluation of a KTS‐WFT site, 10 KTS‐WFT clinicians participated in in‐depth interviews. Analysis of the interviews identified collaboration with child protection as a primary theme. Participants reported a number of barriers to effective collaboration; in particular, participants reported challenges with information sharing and confidentiality, inconsistency in terms of the level and style of collaboration, tensions between the different theoretical paradigms that underpin practice for MH/D&A clinicians vs. child protection workers, and insufficient clarity around processes and expectations. Consistent with the identified barriers, primary recommendations to improve collaboration were to improve information sharing, overcome silo ways of thinking, manage risk together more consistently, and develop consistent processes and expectations. [ABSTRACT FROM AUTHOR]

Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]

Background: Nurses working in intensive care units make autonomous decisions to manage high‐risk vasoactive medications in critically ill patients. Noradrenaline (norepinephrine) is a vasoactive medication commonly administered to patients in intensive care units. The influence of unit culture and environment on nurse‐decision‐making on noradrenaline (norepinephrine) management is unknown. Aims: The study aimed to investigate nurses' perceptions of the impact of interpersonal interactions, socialisation, and the intensive care environment on decision‐making when managing noradrenaline (norepinephrine). Materials & Methods: An exploratory qualitative study applied thematic analysis to focus group data. A purposive sample of nineteen nurses participated in four focus groups at two intensive care units in Melbourne, Australia, from March to June 2021. The COREQ checklist was used to guide study development and no patients or members of the public were involved in focus groups. Results: Three themes were generated from the researcher's interaction with data, Nursing and Medications; Culture and Decision‐making; and a Safe Practice Environment. Nurses reported decision‐making challenges associated with learning to manage noradrenaline (norepinephrine) early in their intensive care career and discussed feelings of isolation due to staffing resources, and the configuration of the intensive care environment. Nurses developed titration and weaning strategies to support decision‐making in the absence of evidence‐based algorithms. Discussion: Empathetic patient allocation early in nurses' intensive care careers facilitated a safer learning environment, and reduced isolation inherent in single room intensive care units. Nurses developed and used titration and weaning strategies, often learnt from other clinicians to manage practice uncertainty. Conclusions: Management of noradrenaline (norepinephrine) is core business for intensive care nurses worldwide. Development of titration and weaning strategies by nurses indicated unmet need for guidelines to support decision‐making. Identifying contextual elements that impact nurse management of high‐risk medications can guide development of environments, resources and policies that support nurse decision‐making, and reduce nurse anxiety and disempowerment. [ABSTRACT FROM AUTHOR]

Rationale: Traditional whole systems of medicine, such as naturopathy, are founded upon holism; a philosophical paradigm consistent with contemporary complexity science. Naturopathic case management is predicated upon the understanding of an intimately interconnected internal physiological and external context of the human organism—potentially indicating a worldview aligned with a complexity perspective. In this study we investigate naturopathic clinical reasoning using a complexity lens with the aim of ascertaining the extent of correspondence between the two. Method: Mind maps depicting case presentations were sought from Australian degree qualified naturopaths. A network mapping was undertaken, which was then analysed in accordance with a complexity science framework using exploratory data analysis and network analysis processes and tools. Results: Naturopathic case schematics, in the form of mind maps (n = 70), were collected, network mapped, and analysed. A total of 739 unique elements and 2724 links were identified across the network. Integral elements across the network were: stress, fatigue, general anxiety, systemic inflammation, gut dysbiosis, and diet. A modularity algorithm detected 11 communities, the primary ones of these representing the nervous system and mood; the gastrointestinal tract, liver, and nutrition; immune function and the immune system; and diet and nutrients. Conclusions: Naturopathic case management is holistic and based on a perspective of an integrated physiology and external context of the human organism. The traditional concept of holism, when subjected to a complexity lens, leads to the emergence of a contemporary holistic paradigm cognisant of the human organism being a complex system. The application of complexity science to investigate naturopathic case management as employed in this study, demonstrates that it is possible to investigate traditional philosophies and principles in a scientific and critical manner. A complexity science research approach may offer a suitable scientific paradigm to develop our understanding of traditional whole systems of medicine. [ABSTRACT FROM AUTHOR]

The purpose was to develop a foot image capture and measurement system with web cameras (the 3-FIS) to provide reliable and valid foot anthropometric measures with efficiency comparable to that of the conventional method of using a handheld anthropometer. Eleven foot measures were obtained from 10 subjects using both methods. Reliability of each method was determined over 3 consecutive days using the intraclass correlation coefficient and root mean square error (RMSE). Reliability was excellent for both the 3-FIS and the handheld anthropometer for the same 10 variables, and good for the fifth metatarsophalangeal joint height. The RMSE values over 3 days ranged from 0.9 to 2.2 mm for the handheld anthropometer, and from 0.8 to 3.6 mm for the 3-FIS. The RMSE values between the 3-FIS and the handheld anthropometer were between 2.3 and 7.4 mm. The 3-FIS required less time to collect and obtain the final variables than the handheld anthropometer. The 3-FIS provided accurate and reproducible results for each of the foot variables and in less time than the conventional approach of a handheld anthropometer. [ABSTRACT FROM AUTHOR]

Background One of the strategies proven most successful in curbing rising rates of childhood obesity involves targeting parents as agents of change. Prior studies have focused on what messages to communicate, but few have investigated how they should be communicated. Objective To identify the channels most effective for communicating with parents of overweight and obese children and understand whether their use of parenting information sources differs from others in the community. Design/setting This study utilizes data from the Longitudinal Study of Australian Children ( LSAC). Families were included if weight and height information was available for parents and children at three data collection points: Waves 1, 2 and 4 (collected 2004, 2006 and 2010, respectively, n = 5107). Analysis A priori and a posteriori segmentation methods identified groups of parents that were similar in the sources used to obtain information about parenting, and examined whether some segments were more likely to have obese children. Results Four segments were identified that differed in their information source use: the 'personal networks', 'books', 'official sources' and 'mixed approach' segments. The 'official sources' and 'mixed approach' segments were most likely to have obese children, and they used doctors, government/community organizations and friends to obtain information on parenting. These segments were also less educated and had lower employment. Conclusions Messages are most likely to reach families with obese children if communicated through doctors, government publications and community organizations. Further, messages targeting social groupings of parents will leverage the power of advice from friends, which is another valuable information source for this group. [ABSTRACT FROM AUTHOR]

Clinical practice guidelines (CPGs) have been shown to improve processes of care and health outcomes, but there is often a discrepancy between recommendations for care and clinical practice. This study sought to explore general practitioner (GP) attitudes towards CPGs, in general and specifically for osteoarthritis (OA), with the implications for translating OA care into practice. A self-administered questionnaire was conducted in January 2013 with a sample of 228 GPs inNewSouth Wales and South Australia. Seventy-nine GPs returned questionnaires (response rate 35%). Nearly all GPs considered that CPGs support decision-making in practice (94%) and medical education (92%). Very few respondents regarded CPGs as a threat to clinical autonomy, and most recognised that individual patient circumstances must be taken into account. Shorter CPG formats were preferred over longer and more comprehensive formats, with preferences being evenly divided among respondents for short, 2-3-page summaries, flowcharts or algorithms and single page checklists. GPs considered accessibility to CPGs to be important, and electronic formats were popular. Familiarity and use of The Royal Australian College of General PractitionersOAGuideline was poor, with most respondents either not aware of it (30%; 95% confidence interval (CI) 27 - 41%), had never used it (19%; 95% CI 12 - 29%) or rarely used it (34%; 95% CI 25-45%). If CPGs are to assist with the translation of evidence into practice, they must be easily accessible and in a format that encourages use. [ABSTRACT FROM AUTHOR]

Time can be thought of as a resource that people need for good health. Healthy behaviour, accessing health services, working, resting and caring all require time. Like other resources, time is socially shaped, but its relevance to health and health inequality is yet to be established. Drawing from sociology and political economy, we set out the theoretical basis for two measures of time relevant to contemporary, market-based societies. We measure amount of time spent on care and work (paid and unpaid) and the intensity of time, which refers to rushing, effort and speed. Using data from wave 9 (N = 9177) of the Household, Income and Labour Dynamics of Australia Survey we found that time poverty (> 80 h per week on care and work) and often or always rushing are barriers to physical activity and rushing is associated with poorer self-rated and mental health. Exploring their social patterning, we find that time-poor people have higher incomes and more time control. In contrast, rushing is linked to being a woman, lone parenthood, disability, lack of control and work-family conflicts. We supply a methodology to support quantitative investigations of time, and our findings underline time's dimensionality, social distribution and potential to influence health. [ABSTRACT FROM AUTHOR]

Introduction and aims: Illicit methamphetamine (MA) use is an important public health concern. There is a dearth of knowledge about effective and cost-effective treatments for methamphetamine (MA) dependence in Australia. This article evaluates the cost-effectiveness of counselling as a treatment option for illicit MA use compared with no treatment option. Design and methods: Data are from 501 individuals recruited into Methamphetamine Treatment Evaluation Study (MATES). The population of MA users from MATES is extrapolated to a total number of 1000 MA users in the intervention group (counselling treatment) and control group (non-treatment group). A decision analytic model is developed that examines the costs and health outcomes [measures as quality adjusted life years (QALYs) gained] for the treatment and comparison group over a 3-year period. A societal perspective is adopted and model inputs are subject to sensitivity and uncertainty analysis to test the robustness of results to parameter variability. Results are discounted by using 3% discount rate and expressed in 2011 Australian dollars. Results: The incremental cost-effectiveness analysis suggests that counselling is a dominant health care intervention, i.e. saves money and is more effective than a do nothing intervention. The incremental difference in costs is −AU$18.36 million (95% CI −AU$22.80 million to −AU$14.31 million) and the incremental difference in QALY is 107 (95% CI −640 to 820) with a probability of 78.64% of counselling being a dominant and cost-effective treatment within the acceptable incremental cost-effectiveness ratio (ICER) of $63 832 per QALY in the Australian society. The results of the sensitivity analysis show that the ICER is most sensitive to change in five major inputs: baseline utility, utility at 3 months, dealing crime costs, property crime costs and fraud crime costs. Discussion and Conclusions: The economic evaluation of the cost-effectiveness of counselling for MA dependence, as a first cost-effectiveness study to assess psychosocial treatment options for MA dependence, shows that greater investment in this cost-effective strategy will produce significant cost-savings and improve health outcomes as well as improve a lot of externality issues associated with drug use. [ABSTRACT FROM AUTHOR]

This study of 1,601 students in the middle years of schooling (Grades 5-8, each student measured twice, 1 year apart) from 200 classrooms in 44 schools sought to identify factors explaining gains and declines in mathematics engagement at key transition points. In multilevel regression modeling, findings showed that compared with Grade 6 students (upper elementary; the reference category), students in Grades 7 (typically the first secondary school year) and 8 have significantly declined in mathematics engagement from their previous year. Notably, in further analyses, these declines were found to be related to student (particularly mathematics self-efficacy and valuing), home (parent valuing of mathematics and availability of a computer for mathematics), classroom (class-average achievement and perceived climate), and school (socio-economic status and ethnic composition) factors. Implications for research and practice are discussed. [ABSTRACT FROM PUBLISHER]

Objective. To determine the economic feasibility in Australian general practices of using a practice nurse (PN)-led care model of chronic disease management. Methods. A cost-analysis of item numbers from the Medicare Benefit Schedule (MBS) was performed in three Australian general practices, one urban, one regional and one rural. Patients (n =254; >18 years of age) with chronic conditions (type 2 diabetes, hypertension, ischaemic heart disease) but without unstable or major health problems were randomised into usual general practitioner (GP) or PN-led care for management of their condition over a period of 12 months. After the 12-month intervention, total MBS item charges were evaluated for patients managed for their stable chronic condition by usual GP or PN-led care. Zero-skewness log transformation was applied to cost data and log-linear regression analysis was undertaken. Results. There was an estimated A$129 mean increase in total MBS item charges over a 1-year period (controlled for age, self-reported quality of life and geographic location of practice) associated with PN-led care. The frequency of GP and PN visits varied markedly according to the chronic disease. Conclusions. Medicare reimbursements provided sufficient funding for general practices to employ PNs within limits of workloads before the new Practice Nurse Incentive Program was introduced in July 2012. [ABSTRACT FROM AUTHOR]

Study design:Cross-section design.Objectives:The development of reliable screen technology for predicting those at risk of depression in the long-term remains a challenge. The objective of this research was to determine factors that classify correctly adults with spinal cord injury (SCI) with depressed mood and to develop a diagnostic algorithm that could be applied for prediction of depressed mood in the long-term.Setting:SCI rehabilitation unit, rehabilitation outpatient clinic and Australian community.Methods:Participants included 107 adults with SCI. The assessment regimen included demographic and injury variables, negative mood states, pain intensity, health-related quality of life and self-efficacy. Participants were divided into those with 'normal' mood versus those with elevated depressed mood. Discriminant function analysis (DFA) was then used to isolate factors that in combination, best classify the presence or absence of depressed mood.Results:At the time of assessment, 24 participants (22.4%) had elevated depressed mood. DFA identified six factors that discriminated between those with depressed mood (P<0.01) and those with normal mood, explaining 61% of the variance. Factors consisted of pain intensity, mental health, emotional and social functioning, self-efficacy and fatigue. DFA correctly classified 91.7% (n=22 of 24) of those with depressed mood and 95.2% (n=79 of 83) of those without. Demographic, injury and physical health function variables were not found to discriminate depressed mood.Conclusion:Clinical implications of applying a diagnostic algorithm for detecting depression in adults with SCI are discussed. Prospective research is needed to test the predictive efficacy of the algorithm. [ABSTRACT FROM AUTHOR]