Gaining a deeper insight into why women use physical violence towards a partner.

Intimate partner violence (IPV) has significant social, economic and health consequences for the victims, their families and society as a whole. There is a need to understand the complex reasons for why IPV occurs in order to be able to intervene and prevent IPV from occurring. The purpose of the current study was to gain a better understanding of the characteristics and motives of women who engage in IPV, specifically physical violence. Qualitative semistructured interviews were used to identify: a) the characteristics and background information of women who have used physical violence towards a partner; b) their motives; c) and whether motives differ from what is reported in the literature for males who have perpetrated IPV. This research found many woman had backgrounds consisting of adverse experiences, parents who used substances, poor family relationships, poor mental health, and came from households characterised by family violence. Romantic relationships were characterised by dysfunctional communication, substance use by their partners and physical violence towards property. Defensive violence, wanting to stop their partners behaviour, retaliation, asserting dominance, and wanting to escape were all motivations reported by women in this study. Coercive control tactics are heavily reported in the literature for male perpetrators of IPV, however were not found in the current study. This research also provided a conceptual replication of the Event Process Model of Family Violence proposed by Stairmand et al. (2020). This research tested Stairmand’s et al. (2020) model using a different population to the original study. Stairmand’s et al. (2020) model assisted in outlining the complicated factors that contributed to engaging in IPV for women. However, the current study could not support the feed-back loop of counterescalation proposed by Stairmand et al. (2020) which potentially is more fitting for chronic or severe IPV. Overall, this research will be influential in the direction of future IPV research using larger samples sizes. Additionally, this research may be used to inform treatment on risk and protective factors for engaging in IPV by intervention and prevention providers

Integrated Prevention and Control of Seasonal Respiratory Infections in Aotearoa New Zealand: next steps for transformative change

Public health measures that successfully eliminated the spread of Covid-19 in Aotearoa New Zealand during 2020 also profoundly reduced the normally high seasonal burden of non-Covid infectious diseases. One outcome of this extraordinary year was that life expectancy in New Zealand actually increased during 2020, the first year of this global pandemic. We should not accept or allow a return to previous levels of illness and death during the winter months. Transformative change will require an integrated approach to infectious disease policy that builds on the knowledge and infrastructure developed during the first two years of the pandemic response. An effective strategy will include generic elements – notably, science-informed strategic leadership, a Tiriti and equity focus, and an upgraded alert level system. We will also need a specific plan for infectious respiratory diseases, including measures to improve indoor air quality, a national mask strategy, and an enhanced system to deliver vaccinations against seasonal respiratory infections. Such an approach can have immediate and long-term benefits, protecting New Zealanders from endemic, epidemic and pandemic infections. We face a potentially difficult winter in 2022, with multiple infectious disease threats. There is an urgent need for integrated policy and action to prevent and control both Covid-19 and more familiar winter season respiratory infections. In the future, 2020 should be seen as the watershed year that triggered a transformative improvement in New Zealand’s poor track record of infectious disease incidence and inequities

Cost benefit analysis of the Warm Up New Zealand: Heat Smart programme

This report summarises the results of an analysis of the costs and benefits of the Warm Up New Zealand: Heat Smart programme. Under the programme, subsidies are provided towards the costs of retrofitting insulation and/or installing clean heating for pre-2000 houses. The benefits that are included in this report are analysed in more detail in three separate papers produced as part of this study that assess the impacts on energy use, health outcomes and producer surpluses, with additional data on employment. The costs of the programme are also assessed in this report and include the costs of the additional insulation and clean heating plus the administrative costs falling on the government. The overall results suggest that the programme as a whole has had sizeable net benefits, with our central estimate of programme benefits being almost five times resource costs attributable to the programme

Taming the 'masculine pioneers'? Changing attitudes towards energy efficiency amongst private landlords and tenants in New Zealand: A case study of Dunedin

New Zealand's housing is some of the poorest quality and hardest to heat in the developed world. The private rented sector in particular offers the worst quality accommodation to the country's poorest and most vulnerable tenants. Previous research has established a range of economic and socio-cultural explanations for the prevalence of poor conditions in private rented accommodation with the 'principal-agent problem' dominating the debate. This paper reports the findings from research in Dunedin, a city with some of the coldest conditions and least energy efficient properties in the country. The study was undertaken in 2015 and involved 30 in-depth interviews with landlords exploring their attitudes towards improving the thermal performance and energy efficiency of their properties. The results revealed a shift in attitudes amongst landlords over a period of about 5 years, with many becoming more amenable to investing in insulation and low energy heat sources. This shift has ostensibly been driven by pressure from tenants who appear to be departing from established cultural norms and becoming intolerant of cold homes and high bills. The study highlights how socio-cultural factors, such as growing expectations regarding warmth and comfort in the home, can disrupt established cultural norms and economic rationales to bring about change

Choice function based hyper-heuristics for multi-objective optimization

A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic

Absraction for Efficient Reinforcement Learning

Successful reinforcement learning requires large amounts of data, compute, and some luck. We explore the ability of abstraction(s) to reduce these dependencies. Abstractions for reinforcement learning share the goals of this abstract: to capture essential details, while leaving out the unimportant. By throwing away inessential details, there will be less to compute, less to explore, and less variance in observations. But, does this always aid reinforcement learning? More specifically, we start by looking for abstractions that are easily solvable. This leads us to a type of linear abstraction. We show that, while it does allow efficient solutions, it also gives erroneous solutions, in the general case. We then attempt to improve the sample efficiency of a reinforcment learner. We do so by constructing a measure of symmetry and using it as an inductive bias. We design and run experiments to test the advantage provided by this inductive bias, but must leave conclusions to future work

Absraction for Efficient Reinforcement Learning

Successful reinforcement learning requires large amounts of data, compute, and some luck. We explore the ability of abstraction(s) to reduce these dependencies. Abstractions for reinforcement learning share the goals of this abstract: to capture essential details, while leaving out the unimportant. By throwing away inessential details, there will be less to compute, less to explore, and less variance in observations. But, does this always aid reinforcement learning? More specifically, we start by looking for abstractions that are easily solvable. This leads us to a type of linear abstraction. We show that, while it does allow efficient solutions, it also gives erroneous solutions, in the general case. We then attempt to improve the sample efficiency of a reinforcment learner. We do so by constructing a measure of symmetry and using it as an inductive bias. We design and run experiments to test the advantage provided by this inductive bias, but must leave conclusions to future work.</p

Home truths and cool admissions: New Zealand housing attributes and excess winter hospitalisation

Background: The ratio of winter to non-winter mortality rates, or excess winter mortality (EWM), is higher in temperate countries, including New Zealand. Many studies suggest housing differences as a possible explanation. Home heating and insulation levels have been found to be associated with health outcomes and some studies have implicated housing faults as contributing to EWM. In contrast, excess winter hospitalisation (EWH) in general, and the contribution housing makes to EWH in particular, has been little explored. Aims: This research aimed to describe EWH, and investigate whether housing attributes were associated with any excess. Method: A retrospective cohort study was conducted of 1,596,126 acute overnight hospitalisations, over 11,477,510,015 person days, between 1 February 2000 and 31 January 2006, using the full National Health Index (NHI) database as the cohort. Using address data, 2,405,070 NHI records were matched to 689,185 Quotable Value NZ Ltd (QV) dwelling records. Winter was defined as 1 June to 30 September. Poisson regressions with robust standard errors were used to calculate both winter:non-winter incidence rate ratios (also known as the excess winter hospitalisation index, or EWHI) and relative rate ratios (RRR). RRRs were used to identify differences in EWHI within demographic variables earlier found to be associated with level of winter excess (sex, age, ethnicity, and Census meshblock rurality, NZDep decile, and annual average minimum outdoor temperature) and within the dwelling attributes (construction decade, insulation era, dwelling type, floor area, condition, tenure index, and capital value). Results: Hospitalisation rates were 8.3% higher in winter than the rest of the year, with 7,166 excess winter hospitalisations per year. All-cause EWHIs were highest in the very young and older people, higher for women than for men, and higher in Māori and Pacific Peoples than in NZ Europeans. However, the higher EWHI for Māori was due to higher rates of respiratory illness (which has the highest EWHI). EWHIs increased with increasing socio-economic deprivation (NZDep decile) and with decreasing annual average minimum temperature, but were lower in Rural Centres than in Main Urban areas. Similar patterns for age, gender, NZDep and temperature were observed in respiratory EWHIs, but while Pacific Peoples had higher respiratory EWHIs than NZ Europeans, Māori did not. Only age showed significant differences in circulatory EWHIs. By dwelling type, EWHIs were higher in Villas (RRR 1.0297, 95% CI 1.0012-1.0591, p=0.041) and in Pre-war Bungalows (RRR 1.0296, 95% CI 1.0089-1.0506, p=0.005) than in Post-war Bungalows, and lower in Quality Bungalows (RRR 0.9781, 95% CI 0.9580-0.9985, p=0.036). EWHIs also increased as the proportion of rental households in a Census meshblock increased, and NZ Europeans living in “Poor” condition dwellings had higher EWHIs than those living in “Superior” dwellings. There was no difference in EWHIs by construction decade or insulation era. Conclusion: Both demographic and environmental factors are associated with differences in EWHIs. Dwelling type is associated with EWH and probably overall hospitalisation rates. Further research to identify whether dwelling design or construction features are behind these differences in EWHIs could suggest areas for public health intervention

Home truths and cool admissions: New Zealand housing attributes and excess winter hospitalisation

Background: The ratio of winter to non-winter mortality rates, or excess winter mortality (EWM), is higher in temperate countries, including New Zealand. Many studies suggest housing differences as a possible explanation. Home heating and insulation levels have been found to be associated with health outcomes and some studies have implicated housing faults as contributing to EWM. In contrast, excess winter hospitalisation (EWH) in general, and the contribution housing makes to EWH in particular, has been little explored. Aims: This research aimed to describe EWH, and investigate whether housing attributes were associated with any excess. Method: A retrospective cohort study was conducted of 1,596,126 acute overnight hospitalisations, over 11,477,510,015 person days, between 1 February 2000 and 31 January 2006, using the full National Health Index (NHI) database as the cohort. Using address data, 2,405,070 NHI records were matched to 689,185 Quotable Value NZ Ltd (QV) dwelling records. Winter was defined as 1 June to 30 September. Poisson regressions with robust standard errors were used to calculate both winter:non-winter incidence rate ratios (also known as the excess winter hospitalisation index, or EWHI) and relative rate ratios (RRR). RRRs were used to identify differences in EWHI within demographic variables earlier found to be associated with level of winter excess (sex, age, ethnicity, and Census meshblock rurality, NZDep decile, and annual average minimum outdoor temperature) and within the dwelling attributes (construction decade, insulation era, dwelling type, floor area, condition, tenure index, and capital value). Results: Hospitalisation rates were 8.3% higher in winter than the rest of the year, with 7,166 excess winter hospitalisations per year. All-cause EWHIs were highest in the very young and older people, higher for women than for men, and higher in Māori and Pacific Peoples than in NZ Europeans. However, the higher EWHI for Māori was due to higher rates of respiratory illness (which has the highest EWHI). EWHIs increased with increasing socio-economic deprivation (NZDep decile) and with decreasing annual average minimum temperature, but were lower in Rural Centres than in Main Urban areas. Similar patterns for age, gender, NZDep and temperature were observed in respiratory EWHIs, but while Pacific Peoples had higher respiratory EWHIs than NZ Europeans, Māori did not. Only age showed significant differences in circulatory EWHIs. By dwelling type, EWHIs were higher in Villas (RRR 1.0297, 95% CI 1.0012-1.0591, p=0.041) and in Pre-war Bungalows (RRR 1.0296, 95% CI 1.0089-1.0506, p=0.005) than in Post-war Bungalows, and lower in Quality Bungalows (RRR 0.9781, 95% CI 0.9580-0.9985, p=0.036). EWHIs also increased as the proportion of rental households in a Census meshblock increased, and NZ Europeans living in “Poor” condition dwellings had higher EWHIs than those living in “Superior” dwellings. There was no difference in EWHIs by construction decade or insulation era. Conclusion: Both demographic and environmental factors are associated with differences in EWHIs. Dwelling type is associated with EWH and probably overall hospitalisation rates. Further research to identify whether dwelling design or construction features are behind these differences in EWHIs could suggest areas for public health intervention