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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]

This study determined stroke and movement accelerometry metrics from a wearable sensor and compared between court surface (grass vs. hard) and match outcome (win vs. loss) during competitive tennis match-play. Eight junior high-performance tennis players wore a trunk-mounted global positioning system, with in-built accelerometer, magnetometer, and gyroscope during singles matches on hard and grass courts. The manufacturer software calculated accelerometer-derived total player load (tPL). A prototype algorithm classified forehands, backhands, serves, and "other" strokes, thereby calculating stroke PL (sPL) from individual strokes. Movement PL (mPL) was calculated as the difference between tPL and sPL, with all metrics reported as absolute and relative (min-1, %, and ·stroke). Analysis of accelerometer load and stroke count metrics was performed through a two-way (surface [grass vs. hard] × match outcome [win vs. loss]) analysis of variance (p < 0.05) and effect sizes (Cohen's d). No interaction effects for surface and match outcome existed for absolute tPL, mPL, and sPL (p > 0.05). Increased mPL% featured on grass courts, whereas sPL% was increased on hard courts (p = 0.04, d = 1.18[0.31-2.02]). Elevated sPL·min-1 existed on hard courts (p = 0.04, d = 1.19[0.32-2.04]), but no differences in tPL·min-1 and mPL·min-1 were evident for surface or outcome (p > 0.05). Relative forehand sPL (FH-sPL·min-1) was higher on hard courts (p = 0.03, d = 1.18[0.31-2.02]) alongside higher forehand counts (p = 0.01, d = 1.29[0.40-2.14]). Hitting demands are heightened on hard courts from increased sPL and counts. Conversely, increased mPL% on grass courts likely reflects the specific movement demands from point-play. Physical preparation strategies during training blocks can be tailored toward movement or hitting loads to suit competitive surfaces. [ABSTRACT FROM AUTHOR]

Live streaming of child sexual abuse (CSA) involves the procurement and viewing of sexual abuse of children across the internet in real time, in exchange for money. These offenses leave little tangible evidence of the offense beyond a financial transaction, and metadata relating to the live-streaming session. This research analyzed the demographic, criminal history, and financial transaction characteristics of 209 individuals who live streamed child sexual abuse. A machine learning clustering technique was implemented to consider whether there were sub-groups present among these offenders, and in particular the prevalence of contact sexual offending among any detected sub-groups. Findings revealed that offenders tend to engage in live streaming around the same age, before making regular transactions with facilitators at brief intervals, with the majority of offenders featuring limited criminal history. This analysis identified a notable sub-group of live-streaming offenders that also engaged in contact sexual offending. This is the first study to empirically demonstrate an intersection between live streaming of CSA, and contact sexual offenses against children and adults. This research highlighted the importance of financial transactions data in detecting, and disrupting this crime type. Further, the identification of an intersection between live-streaming CSA offenders, and contact sexual offenders suggests that these individuals may pose a risk to both local and international communities. [ABSTRACT FROM AUTHOR]

This study aims to understand the effects that role‐diverse online communities have on informal carers, particularly in providing support. Australian Facebook communities used to support those involved in the National Disability Insurance Scheme (NDIS) were explored. Social network analysis of an NDIS‐centred community was conducted, based on 909 publicly visible interactions that occurred in May–June and August–September 2019. Two managers of informal NDIS communities were interviewed, the transcripts of which were analysed using NVivo. Results from the two analyses suggest that both an individual carer's attributes and the collective attributes of the network defined the capability of the network to support the carer, often depending on the experiences and expertise of those offering support. Support was unconstrained by role, though differing goals and expectations often impeded collaboration between roles. The outcomes of support provision were shown to affect not only individuals but also the collective network. However, while effective, community spaces currently lack organisational backing and resources available to informal communities are constrained. Findings drawn from this study, which we believe are applicable to a broader, international context, are three‐fold. Firstly, it is recommended that informal support communities clearly define purpose and create multiple channels to ensure that all participants can meet their needs. Secondly, the benefits of participation to organisations should be further explored. Finally, the use of social network analysis as a method in this study has provided significant insights into the communication patterns and activities of the community under study. Future use of SNA in similar studies may provide further insight into the effectiveness and interactions of community‐based support methods. [ABSTRACT FROM AUTHOR]

Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit. [ABSTRACT FROM AUTHOR]

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs. [ABSTRACT FROM AUTHOR]

Background and aims: Clinical staff are typically poor at predicting alcohol dependence treatment outcomes. Machine learning (ML) offers the potential to model complex clinical data more effectively. This study tested the predictive accuracy of ML algorithms demonstrated to be effective in predicting alcohol dependence outcomes, compared with clinical judgement and traditional linear regression. Design Prospective study. ML models were trained on 1016 previously treated patients (training‐set) who attended a hospital‐based alcohol and drug clinic. ML models (n = 27), clinical psychologists (n = 10) and a 'traditional' logistic regression model (n = 1) predicted treatment outcome during the initial treatment session of an alcohol dependence programme. Setting: A 12‐week cognitive behavioural therapy (CBT)‐based abstinence programme for alcohol dependence in a hospital‐based alcohol and drug clinic in Australia. Participants: Prospective predictions were made for 220 new patients (test‐set; 70.91% male, mean age = 35.78 years, standard deviation = 9.19). Sixty‐nine (31.36%) patients successfully completed treatment. Measurements Treatment success was the primary outcome variable. The cross‐validated training‐set accuracy of ML models was used to determine optimal parameters for selecting models for prospective prediction. Accuracy, sensitivity, specificity, area under the receiver operator curve (AUC), Brier score and calibration curves were calculated and compared across predictions. Findings The mean aggregate accuracy of the ML models (63.06%) was higher than the mean accuracy of psychologist predictions (56.36%). The most accurate ML model achieved 70% accuracy, as did logistic regression. Both were more accurate than psychologists (P < 0.05) and had superior calibration. The high specificity for the selected ML (79%) and logistic regression (90%) meant they were significantly (P < 0.001) more effective than psychologists (50%) at correctly identifying patients whose treatment was unsuccessful. For ML and logistic regression, high specificity came at the expense of sensitivity (26 and 31%, respectively), resulting in poor prediction of successful patients. Conclusions: Machine learning models and logistic regression appear to be more accurate than psychologists at predicting treatment outcomes in an abstinence programme for alcohol dependence, but sensitivity is low. [ABSTRACT FROM AUTHOR]

Background: Competing risk models used for midpregnancy prediction of preterm pre‐eclampsia have shown detection rates (DR) of 85%, at fixed false‐positive rate (FPR) of 10%. The full algorithm used between 19+0 and 24+6 weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms‐like tyrosine kinase‐1 (sFlt‐1) level in MoM. Aims: To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre‐eclampsia. The outcome measured was preterm pre‐eclampsia. Materials and Methods: This is a prospective study including singleton pregnancies at 19–22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt‐1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced. Results: Five hundred and twelve patients were included. Incidence of preterm pre‐eclampsia was 1.6%. Using predicted risk of pre‐eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre‐eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9–13.5, respectively. Addition of sFlt‐1 to the algorithm did not appear to improve performance. sFlt‐1 MoM and PlGF MoM values obtained on the different platforms performed very similarly. Conclusions: Second trimester combined screening for preterm pre‐eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population. [ABSTRACT FROM AUTHOR]

The impact of university rankings has driven scholars to increase productivity, and expanding collaboration has dramatically changed the publishing patterns in the academic publication system. The aim of this study was to develop a scholar classification scheme from publication patterns in academic science. Classification schemes are ways of describing groups that display different clusters of behaviors, approaches, or perspectives, and useful in the development of typologies. In this research, sample data are selected from three representative universities, considered a leading university, a middle‐tier university, and a noncomprehensive university. A final set of 11,427 effective scholars and their 284,128 journal publication records were used to develop the classification scheme via cluster analysis. The results identify six types of scholars, labeled as: singleton (8%), small‐team low performer (16%), small‐team high performer (17%), big‐team strategist (22%), free‐style follower (21%), and life‐time warrior (17%). These six scholar types demonstrate different approaches to publishing that can be used to understand both individual and research team performance across different institutional settings. Additionally, possible future work was identified that uses the scholar classification scheme to define the behavior for agents in an agent‐based model to simulate the strategic‐behavior‐driven academic publication system. [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]

Micro-sensors were used to quantify macro kinematics of classical cross-country skiing techniques and measure cycle rates and cycle lengths during on-snow training. Data were collected from seven national level participants skiing at two submaximal intensities while wearing a micro-sensor unit (MinimaxX™). Algorithms were developed identifying double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn). Technique duration (T-time), cycle rates, and cycle counts were compared to video-derived data to assess system accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, with small mean differences (Mdiff% = −0.2 ± 3.2, −1.5 ± 2.2 and −1.4 ± 6.2) and trivial to small effect sizes (ES = 0.20, 0.30 and 0.13). Very strong correlations were observed for DP, DS, and KDP for T-time (r = 0.87–0.99) and cycle count (r = 0.87–0.99), while mean values were under-reported by the micro-sensor. Incorrect Turn detection was a major factor in technique cycle misclassification. Data presented highlight the potential of automated ski technique classification in cross-country skiing research. With further refinement, this approach will allow many applied questions associated with pacing, fatigue, technique selection and power output during training and competition to be answered. [ABSTRACT FROM PUBLISHER]

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]

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]

Unpaid volunteers occupy many roles and provide crucial services in countries around the world. In Australia, for example, volunteers provide emergency response capabilities to most communities outside of major population centres. Despite the valuable function of this volunteer workforce, evidence indicates declining numbers of volunteer emergency service workers, and suggests that interactions between volunteering and family are implicated in falling numbers. The current study considered volunteering as one component of the community microsystem, and examined volunteering-related Work- Family Conflict ( WFC) and Work- Family Facilitation ( WFF) in N = 682 Australian volunteer firefighters. Structural equation modelling ( SEM) analysis indicated that brigade operational demands had a negative indirect effect on intention to remain through volunteer WFC, as well as a concurrent positive effect on satisfaction. Two volunteering resources were considered (training opportunities and effective leadership), and had positive impacts on volunteer WFF through perceived developmental gain. Although developmental gain had a large positive impact on volunteer satisfaction, volunteer WFF did not. Results indicate that theoretical models of interactions between paid work and family can inform understanding of interactions between voluntary work and family, and thus links between community and family roles. Implications for volunteer emergency services organisations are discussed. [ABSTRACT FROM AUTHOR]