Patient expectations and preferences for follow-up after treatment for lung cancer: a pilot study

Introduction

There is no robust evidence to indicate the most appropriate models of follow-up care for patients who have completed treatment for lung cancer. This pilot study aimed to assess expectations and preferences for follow-up care in a sample of patients who had completed treatment for lung cancer.

Method

Thirty-one patients who had completed treatment for primary lung cancer were recruited. A 13 item self-report survey was developed to elicit patient's preferences and expectations for follow-up. Participants completed the developed survey and clinical and demographic variables were collected.

Results

Factors scored as extremely important by over 80% of respondents focused on care coordination: Being able to see the same doctor or health care professional at each visit (24/83%); Knowing which doctor or nurse to contact if queries arise between follow-up appointments (23/82%); and Knowing the patient can book an appointment or contact a health care professional involved in their care regarding health concerns between visits (25/89%). Patients were supportive of nurse-led follow-up when offered in the context of a model of shared care (21/78%).

Conclusion

This study offers new insight into the expectations and preferences for follow-up of patients with lung cancer, with participants indicated preference for intensive follow-up after the completion of treatment.

Student preferences for bachelor degrees at TAFE: the socio-spatial influence of schools

This report on Student Preferences for Bachelor Degrees at TAFE (Technical and Further Education) institutions is derived from research commissioned by Australia’s National Centre for Student Equity in Higher Education (NCSEHE) hosted at Curtin University and conducted by researchers at Deakin University’s Strategic Centre for Research in Educational Futures and Innovation (CREFI). The report focuses on the influence of schools on their students’ higher education (HE) preferences – particularly their preferences for TAFE bachelor degrees – as recorded by the Victorian and South Australian Tertiary Admissions Centres (VTAC and SATAC). Influence is researched in terms of a school’s socioeconomic status, geographical location and sector. The SATAC data set is considerably smaller, at around 8 per cent of the VTAC data set.Bachelor degrees offered by TAFEs are relatively small in number but a growing higher education option for students in Australia (Gale et al. 2013). The Australian Government’s proposal to extend Commonwealth Supported Places (CSPs) to include Australian higher education not delivered by the nation’s public universities (Department of Education 2014b), is likely to fuel further growth in TAFE bachelor degree offerings. The recent Report of the Review of the Demand Driven Funding System in Australian higher education (Kemp & Norton 2014), which recommended this change, also makes special mention of non-university degree options as something that would be of particular benefit to students from low socioeconomic status backgrounds.The research reported herein is informed by a review of the international research literature, which indicates three main influences on students’ HE preferences: (1) students’ families and communities; (2) the socio-spatial location of their schools; and (3) school practices. This report contributes to understandings on the second of these: the influence of school context (their socio-spatial location) on students’ preferences for TAFE bachelor degrees.The research found that the annual rate of student preferences for TAFE bachelor degrees was relatively stable (at around 1,500 per annum) from 2009 to 2012 but rose significantly (by 30%) in 2013. Students from high socioeconomic status schools (and with an average ATAR of 56.9) were the group that registered the largest number of preferences. The number of preferences for TAFE bachelor degrees lodged by students from metropolitan schools exceeded the preferences of students from schools located in all other regions combined. This might reflect the fact that TAFE institutions offering bachelor degrees tend to be located in metropolitan areas.The research also found that students’ preferences for TAFE bachelor degrees increased after announcement of their Australian Tertiary Admission Rank (ATAR), by between 25 and 30 per cent each year. The post-ATAR increase was most noticeable in the Health and Education fields of study and among students from high socioeconomic status schools. The report concludes that while the public perception of TAFE is that it is a sector primarily for students from low SES backgrounds, this is not reflected in students’ preferences for TAFE bachelor degrees. Instead, the preferences of students from high socioeconomic schools outnumber other SES groups in almost every TAFE-degree field of study. This includes the fields of Health and Education, which are often seen to be typical low SES student choices in universities (Gale & Parker 2013).

Local population preferences toward restoring ecosystem services of a wetland in Thailand

This study reports the findings of a choice experiment designed to explore local population preferences toward wetland ecosystem restoration of Bung Khong Long Wetland in Thailand. By addressing ecological, socioeconomic and cultural dimensions of ecosystem services, the findings provide policy-makers with a richer insight into the interconnections among ecological, socioeconomic and cultural systems in explaining the value of ecosystem services. Gaining an understanding of the trade-offs associated with different interests in ecosystem uses in this community has the capacity to promote wetland management and enhance land use planning. The choice experiment application entails selecting attributes and their levels and developing an experimental design to create the choice sets or hypothetical scenarios for welfare assessment via the questionnaire. The study is based on household level data collected from 780 randomly drawn respondents living around the lake and the data are analysed using the Random Parameter Logit Model with interactions. The findings indicate that the local population derives positive and significant values from the restoration of wetland ecosystem services, indicating caution is needed in the decision-making processes involving sensitive environments faced with competing uses. Socioeconomic and attitudinal characteristics of respondents are important factors influencing willingness to pay, implying community preferences are important in the effectiveness of environmental conservation efforts in this community. The cultural values associated with the wetland are significant suggesting that incorporating culture preferences may be a key factor in supporting wetland conservation.

Artificial selection for food colour preferences

Colour is an important factor in food detection and acquisition by animals using visually based foraging. Colour can be used to identify the suitability of a food source or improve the efficiency of food detection, and can even be linked to mate choice. Food colour preferences are known to exist, but whether these preferences are heritable and how these preferences evolve is unknown. Using the freshwater fish Poecilia reticulata, we artificially selected for chase behaviour towards two different-coloured moving stimuli: red and blue spots. A response to selection was only seen for chase behaviours towards the red, with realized heritabilities ranging from 0.25 to 0.30. Despite intense selection, no significant chase response was recorded for the blue-selected lines. This lack of response may be due to the motion-detection mechanism in the guppy visual system and may have novel implications for the evolvability of responses to colour-related signals. The behavioural response to several colours after five generations of selection suggests that the colour opponency system of the fish may regulate the response to selection.

People making choices: the support needs and preferences of people with psychosocial disability: project summary

The aim of this project was to improve understanding of the choices that people with psychosocial disability would make about support for priority life goals if they were offered individualised funding packages. This was timely given the inclusion of psychosocial disability in the National Disability Insurance Scheme (NDIS), which has been designed to enable Australians with disability the opportunity to exercise choice and control in the pursuit of their goals and the planning and delivery of their supports (Commonwealth of Australia, 2013b).

Costs and advance directives at the end of life: a case of the ‘Coaching Older Adults and Carers to have their preferences Heard (COACH)’ trial

BACKGROUND: Total costs associated with care for older people nearing the end of life and the cost variations related with end of life care decisions are not well documented in the literature. Healthcare utilisation and associated health care costs for a group of older Australians who entered Transition Care following an acute hospital admission were calculated. Costs were differentiated according to a number of health care decisions and outcomes including advance directives (ADs).

METHODS: Study participants were drawn from the Coaching Older Adults and Carers to have their preferences Heard (COACH) trial funded by the Australian National Health and Medical Research Council. Data collected included total health care costs, the type of (and when) ADs were completed and the place of death. Two-step endogenous treatment-regression models were employed to test the relationship between costs and a number of variables including completion of ADs.

RESULTS: The trial recruited 230 older adults with mean age 84 years. At the end of the trial, 53 had died and 80 had completed ADs. Total healthcare costs were higher for younger participants and those who had died. No statistically significant association was found between costs and completion of ADs.

CONCLUSION: For our frail study population, the completion of ADs did not have an effect on health care utilisation and costs. Further research is needed to substantiate these findings in larger and more diverse clinical cohorts of older people.

Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol

INTRODUCTION: Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting. METHODS AND ANALYSIS: We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results. ETHICS AND DISSEMINATION: This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.

Identifying emerging hotel preferences using emerging pattern mining technique

Hotel managers continue to find ways to understand traveler preferences, with the aim of improving their strategic planning, marketing, and product development. Traveler preference is unpredictable for example, hotel guests used to prefer having a telephone in the room, but now favor fast Internet connection. Changes in preference influence the performance of hotel businesses, thus creating the need to identify and address the demands of their guests. Most existing studies focus on current demand attributes and not on emerging ones. Thus, hotel managers may find it difficult to make appropriate decisions in response to changes in travelers' concerns. To address these challenges, this paper adopts Emerging Pattern Mining technique to identify emergent hotel features of interest to international travelers. Data are derived from 118,000 records of online reviews. The methods and findings can help hotel managers gain insights into travelers' interests, enabling the former to gain a better understanding of the rapid changes in tourist preferences.

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

Learning preference models from human generated data is an important task in modern information processing systems. Its popular setting consists of simple input ratings, assigned with numerical values to indicate their relevancy with respect to a specific query. Since ratings are often specified within a small range, several objects may have the same ratings, thus creating ties among objects for a given query. Dealing with this phenomena presents a general problem of modelling preferences in the presence of ties and being query-specific. To this end, we present in this paper a novel approach by constructing probabilistic models directly on the collection of objects exploiting the combinatorial structure induced by the ties among them. The proposed probabilistic setting allows exploration of a super-exponential combinatorial state-space with unknown numbers of partitions and unknown order among them. Learning and inference in such a large state-space are challenging, and yet we present in this paper efficient algorithms to perform these tasks. Our approach exploits discrete choice theory, imposing generative process such that the finite set of objects is partitioned into subsets in a stagewise procedure, and thus reducing the state-space at each stage significantly. Efficient Markov chain Monte Carlo algorithms are then presented for the proposed models. We demonstrate that the model can potentially be trained in a large-scale setting of hundreds of thousands objects using an ordinary computer. In fact, in some special cases with appropriate model specification, our models can be learned in linear time. We evaluate the models on two application areas: (i) document ranking with the data from the Yahoo! challenge and (ii) collaborative filtering with movie data. We demonstrate that the models are competitive against state-of-the-arts.

Biased experts and similarity based weights in preferences aggregation

In a group decision making setting, we consider the potential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts' inputs during the aggregation process can minimize the influence the biased expert is able to exert.

A discrete choice experiment to examine the preferences of patients with cancer and their willingness to pay for different types of health care appointments

BACKGROUND: This study sought to understand the preferences of patients with cancer and the trade-offs between appointment attributes using discrete choice experiment (DCE). METHODS AND STUDY DESIGN: Patients with cancer at 3 hospitals completed a self-administered DCE. Each scenario described 6 attributes: expertise of health care professionals (HCPs), familiarity of doctors with patients' medical history, waiting time, accompaniment by family/friends, travel time, and out-of-pocket costs. Patient preferences were estimated using logistic regression. Willingness to pay (WTP) estimates were derived from regression coefficients. RESULTS: Of 512 patients contacted, 185 returned the questionnaire. The mean age was 61 years, and 60% of respondents were female. The mean time since cancer diagnosis was 34 months, 90% had received treatment; and 61% had early-stage disease. The most important attributes were expertise and familiarity of doctors with patients' medical history; distance traveled was least likely to influence patient preferences. The WTP analysis estimated that patients were willing to pay $680 (95% CI, 470-891) for an appointment with a specialist, $571 (95% CI, 388-754) for doctors familiar with their history, $422 (95% CI, 262-582) for shorter waiting times, $399 (95% CI, 249-549) to be accompanied by family/friends, and $301 (95% CI, 162-441) for shorter traveling times. Male patients had a stronger preference for accompaniment by family/friends. The expertise of HCP was the most important attribute for patients regardless of geographic remoteness. CONCLUSIONS: Our study can assist the development of patient-centered health care models that improve patient access to experienced HCPs, support the role of primary care providers during the cancer journey, and educate patients about the roles of non-oncology HCPs to cope with increasing demand for cancer care.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

Taste preferences of children

In this book, the authors present topical research data in the study of the sense of taste

Chocolate and children's food and flavour preferences

Chocolate in Health and Nutrition represents the first comprehensive compilation of the newest data on the actions of the flavonoids and microorganisms associated with the beneficial effects of chocolate.

Local gambling preferences and corporate innovative success

This paper examines the role of local attitudes toward gambling on corporate innovative activity. Using a county's Catholics-to-Protestants ratio as a proxy for local gambling preferences, we find that firms located in gambling-prone areas tend to undertake riskier projects, spend more on innovation, and experience greater innovative output. We contrast the local gambling effect with chief executive officer (CEO) overconfidence, another behavioral effect reported to influence innovation. We find that local gambling preferences are a stronger determinant of innovative activity, with CEO overconfidence being more relevant to innovation in areas where gambling attitudes are strong.