Victimization experiences of adolescents in Malaysia

Purpose There has been little community-based research regarding multiple-type victimization experiences of young people in Asia, and none in Malaysia. This study aimed to estimate prevalence, explore gender differences, as well as describe typical perpetrators and family and social risk factors among Malaysian adolescents. Methods A cross-sectional survey of 1,870 students was conducted in 20 randomly selected secondary schools in Selangor state (mean age: 16 years; 58.8% female). The questionnaire included items on individual, family, and social background and different types of victimization experiences in childhood. Results Emotional and physical types of victimization were most common. A significant proportion of adolescents (22.1%) were exposed to more than one type, with 3% reporting all four types. Compared with females, males reported more physical, emotional, and sexual victimization. The excess of sexual victimization among boys was due to higher exposure to noncontact events, whereas prevalence of forced intercourse was equal for both genders (3.0%). Although adult male perpetrators predominate, female adults and peers of both genders also contribute substantially. Low quality of parent–child relationships and poor school and neighborhood environments had the strongest associations with victimization. Family structure (parental divorce, presence of step-parent or single parent, or household size), parental drug use, and rural/urban location were not influential in this sample. Conclusion This study extends the analysis of multiple-type victimization to a Malaysian population. Although some personal, familial, and social factors correlate with those found in western nations, there are cross-cultural differences, especially with regard to the nature of sexual violence based on gender and the influence of family structure.

Food insecurity among Australian children : potential determinants, health and developmental consequences

Children in food-insecure households may be at risk of poor health, developmental or behavioural problems. This study investigated the associations between food insecurity, potential determinants and health and developmental outcomes among children. Data on household food security, socio-demographic characteristics and children’s weight, health and behaviour were collected from households with children aged 3–17 years in socioeconomically disadvantaged suburbs by mail survey using proxy-parental reports (185 households). Data were analysed using logistic regression. Approximately one-in-three households (34%) were food insecure. Low household income was associated with an increased risk of food insecurity [odds ratio (OR), 16.20; 95% confidence interval (CI), 3.52–74.47]. Children with a parent born outside of Australia were less likely to experience food insecurity (OR, 0.42; 95% CI, 0.19–0.93). Children in food-insecure households were more likely to miss days from school or activities (OR, 3.52; 95% CI, 1.46–8.54) and were more likely to have borderline or atypical emotional symptoms (OR, 2.44; 95% CI, 1.11–5.38) or behavioural difficulties (OR, 2.35; 95% CI, 1.04–5.33). Food insecurity may be prevalent among socioeconomically disadvantaged households with children. The potential developmental consequences of food insecurity during childhood may result in serious adverse health and social implications.

Diversity, disruption, continuity: Parenting and social and emotional wellbeing amongst Aboriginal peoples and Torres Strait Islanders

This paper reviews the diversity in parenting values and practices amongst Aboriginal peoples and Torres Strait Islanders. Firstly, issues arising from the historical traumatic disruption of families’ attachments are discussed, Then the contribution Indigenous parenting makes to the development of healthy and vulnerable individuals becomes the central focus. Family therapists can draw from a broad understanding of the diversity of parenting values and practices in the context of a strength-based approach.

Taxonomy for rainfall events based on pollutant wash-off potential in urban areas

Conventional rainfall classification for modelling and prediction is quantity based. This approach can lead to inaccuracies in stormwater quality modelling due to the assignment of stochastic pollutant parameters to a rainfall event. A taxonomy for natural rainfall events in the context of stormwater quality is presented based on an in-depth investigation of the influence of rainfall characteristics on stormwater quality. In the research study, the natural rainfall events were classified into three types based on average rainfall intensity and rainfall duration and the classification was found to be independent of the catchment characteristics. The proposed taxonomy provides an innovative concept in stormwater quality modelling and prediction and will contribute to enhancing treatment design for stormwater quality mitigation.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

The effect of implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign documentation

Introduction and objectives Early recognition of deteriorating patients results in better patient outcomes. Modified early warning scores (MEWS) attempt to identify deteriorating patients early so timely interventions can occur thus reducing serious adverse events. We compared frequencies of vital sign recording 24 h post-ICU discharge and 24 h preceding unplanned ICU admission before and after a new observation chart using MEWS and an associated educational programme was implemented into an Australian Tertiary referral hospital in Brisbane. Design Prospective before-and-after intervention study, using a convenience sample of ICU patients who have been discharged to the hospital wards, and in patients with an unplanned ICU admission, during November 2009 (before implementation; n = 69) and February 2010 (after implementation; n = 70). Main outcome measures Any change in a full set or individual vital sign frequency before-and-after the new MEWS observation chart and associated education programme was implemented. A full set of vital signs included Blood pressure (BP), heart rate (HR), temperature (T°), oxygen saturation (SaO2) respiratory rate (RR) and urine output (UO). Results After the MEWS observation chart implementation, we identified a statistically significant increase (210%) in overall frequency of full vital sign set documentation during the first 24 h post-ICU discharge (95% CI 148, 288%, p value <0.001). Frequency of all individual vital sign recordings increased after the MEWS observation chart was implemented. In particular, T° recordings increased by 26% (95% CI 8, 46%, p value = 0.003). An increased frequency of full vital sign set recordings for unplanned ICU admissions were found (44%, 95% CI 2, 102%, p value = 0.035). The only statistically significant improvement in individual vital sign recordings was urine output, demonstrating a 27% increase (95% CI 3, 57%, p value = 0.029). Conclusions The implementation of a new MEWS observation chart plus a supporting educational programme was associated with statistically significant increases in frequency of combined and individual vital sign set recordings during the first 24 h post-ICU discharge. There were no significant changes to frequency of individual vital sign recordings in unplanned admissions to ICU after the MEWS observation chart was implemented, except for urine output. Overall increases in the frequency of full vital sign sets were seen.

Using a design led approach to emotional business modelling

Prototyping is an established and accepted practice used by the design community. Prototypes play a valuable role during the design process and can greatly affect the designed outcome. The concept of a business model prototype, however, is not well understood by the design and business communities. Design industry trends indicate a move away from product and service innovation towards business model innovation. Therefore, it stands to reason that the role of prototypes and prototyping in this context should also be considered. This paper is conceptual and presents a process for creating and enabling business model prototypes. Specifically, the focus is on building emotional connections across the value chain to enable internal growth within firms. To do this, the authors‟ have relied on personal observations and critical reflection from multiple industry engagements. The outcomes of this critical reflective practice are presented and the opportunities and challenges for this approach are discussed. Future research opportunities are also detailed and presented within the context of the emotional business model.

Depression: an emotional obstacle to seeking medical advice for infertility

Objective: To investigate the mental and general health of infertile women who had not sought medical advice for their recognized infertility and were therefore not represented in clinical populations. Design: Longitudinal cohort study.Setting Population based.Patient(s) Participants in the Australian Longitudinal Study on Women's Health aged 28-33 years in 2006 who had ever tried to conceive or had been pregnant (n = 5,936).Intervention(s) None.Main Outcome Measure(s) Infertility, not seeking medical advice. Result(s): Compared with fertile women (n = 4,905), infertile women (n = 1,031) had higher odds of self-reported depression (odds ratio [OR] 1.20, 95% confidence interval [CI] 1.01-1.43), endometriosis (5.43, 4.01-7.36), polycystic ovary syndrome (9.52, 7.30-12.41), irregular periods (1.99, 1.68-2.36), type II diabetes (4.70, 1.79-12.37), or gestational diabetes (1.66, 1.12-2.46). Compared with infertile women who sought medical advice (n = 728), those who had not sought medical advice (n = 303) had higher odds of self-reported depression (1.67, 1.18-2.37), other mental health problems (3.14, 1.14-8.64), urinary tract infections (1.67, 1.12-2.49), heavy periods (1.63, 1.16-2.29), or a cancer diagnosis (11.33, 2.57-49.89). Infertile women who had or had not sought medical advice had similar odds of reporting an anxiety disorder or anxiety-related symptoms. Conclusion(s): Women with self-reported depression were unlikely to have sought medical advice for infertility. Depression and depressive symptoms may be barriers to seeking medical advice for infertility.

Contraceptive changes after reproductive events among Australian women born in 1973 to 1978: A longitudinal study from 1996 to 2009

Background Women change contraception as they try to conceive, space births, and limit family size. This longitudinal analysis examines contraception changes after reproductive events such as birth, miscarriage or termination among Australian women born from 1973 to 1978 to identify potential opportunities to increase the effectiveness of contraceptive information and service provision. Methods Between 1996 and 2009, 5,631 Australian women randomly sampled from the Australian universal health insurance (Medicare) database completed five self-report postal surveys. Three longitudinal logistic regression models were used to assess the associations between reproductive events (birth only, birth and miscarriage, miscarriage only, termination only, other multiple events, and no new event) and subsequent changes in contraceptive use (start using, stop using, switch method) compared with women who continued to use the same method. Results After women experienced only a birth, or a birth and a miscarriage, they were more likely to start using contraception. Women who experienced miscarriages were more likely to stop using contraception. Women who experienced terminations were more likely to switch methods. There was a significant interaction between reproductive events and time indicating more changes in contraceptive use as women reach their mid-30s. Conclusion Contraceptive use increases after the birth of a child, but decreases after miscarriage indicating the intention for family formation and spacing between children. Switching contraceptive methods after termination suggests these pregnancies were unintended and possibly due to contraceptive failure. Women’s contact with health professionals around the time of reproductive events provides an opportunity to provide contraceptive services.