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This study investigated everyday associations between one key facet of mindfulness (allocating attention to the present moment) and pain. In Study 1, 89 community-dwelling adults (33–88 years; Mage = 68.6) who had experienced a stroke provided 14 daily end-of-day present-moment awareness and pain ratings. In Study 2, 100 adults (50–85 years; Mage = 67.0 years) provided momentary present-moment awareness and pain ratings three times daily for 10 days. Multi-level models showed that higher trait present-moment awareness was linked with lower overall pain (both studies). In Study 1, participants reported less pain on days on which they indicated higher present-moment awareness. In Study 2, only individuals with no post-secondary education reported less pain in moments when they indicated higher present-moment awareness. Findings add to previous research using global retrospective pain measures by showing that present-moment awareness might correlate with reduced pain experiences, assessed close in time to when they occur.
In the last ten to fifteen years, it has become common for researchers to collect both quantitative (Sue & Ritter, 2012) and qualitative data (Jowett, Peel, & Shaw, 2011) online. The Covid-19 pandemic has increased the importance of this process and accelerated it in many disciplines (Torrentira, 2022).
In addition to convenience, recent work suggests that online data collection may be more valid than face-to-face data collection for some populations. This would mean that online data collection may be the most valid and effective for this age group (Barratt, 2012).
Adolescents with an eating disorder tend to be more open about their symptoms via impersonal online data collection than they are in a face-to-face conversation. Symptom underrating has been documented in face-to-face interviews, because “of feelings of shame elicited by the loss of anonymity during face to-face interviews” leading to face-to-face denial, whereas a self-report questionnaire allows for more privacy and hence honesty while answering questions (Berg et al. 2011). This is especially key in the diagnosis of Anorexia Nervosa (AN), as AN patients minimize, deny, and/or fail to recognize their symptoms (Passi, Bryson and Lock 2003).
Given the benefits of collecting data online for both researchers and participants, it is important to determine the quality of the data collected online to guide its use and interpretation. More evidence is needed to confirm the equivalence of online and face-to-face interview data. The current study examines the equivalence of semi-structured interview data collected online versus original face-to-face interviews.
The Eating Disorder Examination (EDE), assessing psychopathology of eating disorders, was administered to 49 adolescents meeting ICD 10 criteria for anorexia nervosa or atypical anorexia nervosa. The same diagnostic interview was administered twice, once via face-to-face and once as an online version, within a week. Method order was counterbalanced among participants and temporal stability was controlled. The Eating Disorder Inventory-2 (EDI-2) was used as a control variable.
Both the equivalence test and the null hypothesis test were significant for the sum score of the EDE. Measures of psychopathology in eating disorders demonstrated equivalence across face-to-face and online format of the EDE.
The aim was to examine the equivalence of face-to-face and online methodologies, controlling for temporal change in the variable under investigation over one week and order of administration. Results demonstrate equivalence across face-to-face and online format of the EDE. These findings suggest that data obtained using EDE online can be interpreted in comparison with normative data obtained in the face-to-face Interview and that corrections trough transformations are not necessary.
Less is known about the relationship between conduct disorder (CD), callous–unemotional (CU) traits, and positive and negative parenting in youth compared to early childhood. We combined traditional univariate analyses with a novel machine learning classifier (Angle-based Generalized Matrix Learning Vector Quantization) to classify youth (N = 756; 9–18 years) into typically developing (TD) or CD groups with or without elevated CU traits (CD/HCU, CD/LCU, respectively) using youth- and parent-reports of parenting behavior. At the group level, both CD/HCU and CD/LCU were associated with high negative and low positive parenting relative to TD. However, only positive parenting differed between the CD/HCU and CD/LCU groups. In classification analyses, performance was best when distinguishing CD/HCU from TD groups and poorest when distinguishing CD/HCU from CD/LCU groups. Positive and negative parenting were both relevant when distinguishing CD/HCU from TD, negative parenting was most relevant when distinguishing between CD/LCU and TD, and positive parenting was most relevant when distinguishing CD/HCU from CD/LCU groups. These findings suggest that while positive parenting distinguishes between CD/HCU and CD/LCU, negative parenting is associated with both CD subtypes. These results highlight the importance of considering multiple parenting behaviors in CD with varying levels of CU traits in late childhood/adolescence.
Aim of the study was assessing motivation to change in a clinical sample of adolescents with anorexia nervosa and its relation to specific cognitions and behaviours.
N= 77 patients with anorexia nervosa (75 female, 2 males, mean age = 15.8, SD = 1.3) were evaluated with Anorexia Nervosa Stage of Change Questionnaire (ANSOCQ) measuring motivation to recover and further questionnaires evaluating treatment and course of the disorder. The latter included Eating Disorder Inventory (EDI-2), the Eating Attitude Test (EAT), the Pros and Cons of Anorexia nervosa questionnaire (PCAN), and the Body Image Questionnaire (FKAN). Data were collected at treatment onset (T1), follow-up was after 12 months (T2).
At T1 motivation to change was significantly related to the EDI subscale measuring drive for thinness (p < .001) and body dissatisfaction (p< .01), to the PCAN pros scales measuring appearance (p< .01) and safe/structure (p< .01), to the EAT scale measuring dieting (p< .001) and the FKAN subscale measuring the feeling of body massivity (p< .001).The stage of motivation to change at treatment onset was very low in the sample (67% in stage 1 = precontemplation or 2 = contemplation). There was no relation between motivation at T1 and BMI at T2 but the increase of motivation between T1 and T2 predicted a better outcome (BMI at T2).
Motivation to change is related to specific cognitions and behaviours in adolescent patients with anorexia nervosa. Increase of treatment motivation during the first months of treatment seems to be predictive for outcome.
The aim of the study was assessing differences in selected psychosocial and behavioural variables between a referred sample of patients with an eating disorder, a non-referred risk sample with eating problems, and a healthy control group.
There were N=100 patients (95 females and 5 males, Mean age = 15.8, SD = 1.3) in the referred sample. The two matched non-referred groups of each N=215 participants (95% females, 5% males; Mean age =16.1, SD = 1.5) stem from the Zurich Adolescent Psychology- and Psychopathology-Study (ZAPPS). Emotional and behavioural problems were assessed using the Youth Self Report (YSR) and a depression scale. In addition, the participants responded to questionnaires covering life events, self-related cognitions, coping capacities, perceived parental behaviour, and family climate.
Compared to both non-referred groups, the referred sample had significantly higher scores on the social withdrawn, anxious/depressed and thought problems scale of the YSR, on the depression and the life events scales, and significantly lower scores on the self esteem scale. Compared to the referred sample, the non-referred risk sample had significantly higher scores on the YSR-scale measuring externalizing problems and the perceived parental rejection scale, but lower scores on the family climate scales measuring cohesion and adaptability.
The three groups were differentiated by various psychosocial and behavioural variables. Externalizing problems and indicators of a poor family climate were more common in a non-referred high-risk group with eating problems than in a referred sample with clinical eating disorders.
This is a retrospective study over a 5-year period. In total, 3139 embryos were individually cryopreserved (Cryotop®) and warmed using the Kitazato vitrification/warming kit. They were classified into three categories based on their expansion degree. Transfer, implantation and pregnancy rates were assessed for each embryo category and compared using SPSS (Statistical Package for the Social Sciences) software. In total, 1139 couples enrolled in infertility treatment programme benefitted from embryo vitrification at day 5. After warming, embryos belonging to the three categories showed similar success rates. Although there was a trend towards better outcomes when grade 3 embryos were transferred, the differences did not reach statistical significance: implantation rates (n fetal sac/n embryo transferred) grade 1: 21.9%, grade 2: 22.7% and grade 3: 30.3% (=0.19). Pregnancy rate (n clinical pregnancy/n transfer) (21.9%, 22.7%, 30.3%, respectively; P=0.11). Miscarriage rate was not statistically different in the three categories (14.5%, 20.4%, 20%, respectively, P=0.51). Our overall results show that it is worth vitrifying slow kinetics embryos as they provide a non-negligible chance to give rise to a pregnancy.
Here we evaluated the effect of fermented milk supplemented with whey protein (approximately 80 % protein), probiotic (Bifidobacterium animalis subsp. lactis BB12) and pomegranate juice (Punica granatum L.) on the physical performance, intestinal motility and villi structure, inflammatory markers and intestinal microbiota of rats under high-intensity acute exercise. In all, twenty-four Wistar rats were separated into groups: control (Ctrl), supplemented (Supp), exercised (Exe) and exercised and supplemented (Exe+Supp). Rats in the Supp groups received fermented milk during 6 weeks by oral administration. At the end of the supplementation period, the Exe groups were submitted to high-intensity acute exercise on a treadmill. We found that intense acute exercise caused changes in the intestinal villi interspace, changes in the proportion of Lactobacillus species and an increase in Clostridium species, as well as a decrease in intestinal motility. Supplementation increased intestinal motility, and maintained the intestinal villi interspace and the natural microbiota proportions of the exercised rats. Physical performance was not improved by fermented milk supplementation. We conclude that the fermented milk containing whey protein, B. animalis (BB12) and pomegranate juice can re-establish intestinal microbiota and protect the animals from the undesirable effects of intense acute exercise.
This book looks at the behavior of individuals at risk, insurance industry decision makers, and policy makers at the local, state, and federal levels involved in the selling, buying, and regulating of insurance. It compares their actions to those predicted by benchmark models of choice derived from classical economic theory. Where actual choices stray from predictions, the behavior is considered to be anomalous. We attempt to understand why these anomalies sometimes occur and sometimes do not, in many cases using insights from behavioral economics. We then consider if and how such behavioral anomalies could be modified.
This book is in no way a defense of the insurance industry nor an attack on it. Neither is it a consumer guide to purchasing insurance, although we believe that consumers will benefit from the insights it contains. Rather, we describe in this book situations in which public policy and the insurance industry’s collective posture need to change. This may require incentives, rules, and institutions that will help reduce inefficient and anomalous behaviors and encourage behavior that will improve individual and social welfare.
The benchmark model of supply assumes that competitive insurance firms know the loss probabilities and outcomes of the risks they are insuring against and base their premiums on this information. Furthermore, they are able to effortlessly change their premiums to reflect updates in their estimates of the risk. Insurance firms have access to the capital markets (at competitive interest rates) for any needed funds, even after experiencing a large loss. Investors who supply capital to insurers hold diversified portfolios. Losses are independent of one another, so that the law of large numbers minimizes the likelihood of an unexpectedly large total loss and makes it very unlikely an insurer will have to declare insolvency.
In that ideal world, insurance firms are also assumed to have accurate information on the risks of their customers and choose actions that maximize their expected profits. Under this model, firms should be willing to supply virtually any amount of insurance that buyers are likely to find attractive. The premiums they would charge are just high enough to cover their expected claims, including loading costs, which yield a rate of return on capital that investors could have earned elsewhere in the private market. The supply curve of insurance would be virtually horizontal because insurance uses only a tiny fraction of the global capital pool. In other words, the price of coverage should be largely unaffected by variations in the demand for insurance coverage or insurer demand for additional capital.
The classical approach to economics tends to elegance and simplicity, as we saw in the benchmark models of supply and demand. The outcome of a competitive insurance market generally means resources are allocated efficiently and outputs are produced at lowest cost; consumer welfare is maximized, given the resources available to the economy. The only reason for intervention by the public sector is to correct any inequities from the resulting premiums, such as providing some type of subsidy to low-income residents currently residing in hazard-prone areas who cannot afford homeowners’ insurance or to low-income households that might fail to buy health insurance without financial assistance.
But the real world is a considerably messier place. Individuals have difficulty understanding the purposes and concepts of insurance; firms often do not provide coverage at premiums that reflect risk. This chapter presents and analyzes anecdotal evidence of unusual insurance behavior reported by the media. Few of these journalistic examples use benchmark economic models to measure alleged mistakes by consumers considering buying insurance or insurers determining whether to offer coverage against a particular risk.
When Judy’s sister discovered she had cancer, Judy was frightened. Their mother had died of cancer and Judy became preoccupied with the possibility that a genetic link might endanger her, too. Not long after her sister’s diagnosis, Judy bought an expensive insurance policy that promised to pay her money on top of any medical benefits if she got cancer (but not if she contracted another disease).
Doug’s new car, an expensive Lexus sports car, was his dream come true. He maintained it meticulously and, thinking ahead, took out an insurance policy on the car with a very low $500 deductible, even though that increased his premium considerably. That way, he figured, repairs to his car would be nearly covered even if someone in a parking lot simply dented his fender. Three months later, Doug’s attention momentarily lapsed when his cell phone rang and he plowed into the car ahead of him at a stoplight. The body shop estimated damages to the Lexus at $1,500, well over Doug’s $500 deductible. Yet Doug chose to pay the entire cost out of pocket, fearful that making a claim would drive his insurance premiums even higher.
The benchmark model of demand developed in Chapter 2, based on expected utility theory, postulates a world in which the collection and processing of relevant information is costless to consumers, risk is perceived accurately, and the individual is assumed capable of choosing the amount of insurance that maximizes his or her expected utility. As long as people are risk averse, they are willing to pay a premium greater than the expected value of losses from a set of prespecified risky events. The maximum amount an individual is willing to pay for a given level of coverage depends on that individual’s degree of risk aversion. The optimal amount of coverage is determined by comparing the benefits of more financial protection should a disaster occur with the additional premiums for purchasing this additional coverage.
In the last chapter, we explored extensions to the benchmark model of demand that introduced imperfect information and search costs, but maintained the assumption that individuals choose options that maximize their expected utility. This chapter further relaxes some of the benchmark model assumptions and explores different theories of choice and behavior under risk. As we will show, commonly observed behaviors inconsistent with expected utility maximization can be explained by other theories supported by data from experiments, field studies, and consumers’ actual insurance-related decisions.
This chapter focuses on empirical examples of specific types of anomalies by insurers and those who supply capital to insurers. They may be caused partially by some of the concerns of insurance firm managers discussed in the previous chapter, such as fear of insolvency and the impact that this would have on their future job prospects. They may also be due to the effect rating agencies and regulators may have on the premiums insurers can or want to charge and the reserves they are forced to hold. Other deviations from benchmark supply behavior may be a result of the decision processes and heuristics used by insurance managers.
We first consider specific examples of insurer firm and manager behavior that do not adhere to the benchmark supply model and attempt to identify which, if any, of these behaviors can be classified as anomalies. We then consider anomalous behavior on the part of investors and other capital suppliers to insurance firms that create supply problems for insurers.
DECISION TO OFFER OR NOT OFFER TERRORISM COVERAGE
Insurers, like everyone else, have difficulty dealing with the uncertainty associated with terrorism. The likelihood of an attack is highly ambiguous and the actions taken by terrorists may change depending upon what protective measures are undertaken by those at risk. This latter feature distinguishes terrorism risk from other low-probability, high-consequence risks, such as hurricanes and other natural catastrophes where nature does not try to outwit the adoption of preventive measures and where catastrophe models using scientific and engineering data can help insurers determine premiums.
Up to this point we have been primarily concerned with private insurance markets in which buyers and/or sellers behave in different ways than postulated by conventional economic norms of individual and firm choices under risk. But there are some kinds of insurance for large segments of the population that are financed and sometimes produced by the public rather than the private sector: flood insurance discussed in the previous chapter, insurance for retirement income (Social Security), insurance against job loss (unemployment insurance), and insurance for medical care costs (Medicare and Medicaid, to be expanded under health care reform).
Although potential anomalous buyer behavior provides some of the rationale for the development of these types of publicly financed insurance (as was the case with flood insurance when the private sector stopped offering coverage after the Mississippi floods of 1927), an equally if not more important rationale for such coverage is based on equity or distributional considerations. More specifically, some people have incomes in retirement or after job loss that others in society judge to be too low; some people would not obtain what others regard as adequate amounts of medical care. These are deemed unacceptable situations that the public sector steps in to correct.
Now that we have described the types of demand and supply anomalies relative to the benchmark models, we analyze several real-world insurance markets to illustrate when behavior would be classified as anomalous. At the heart of that determination are two questions:
Do consumers make decisions regarding insurance purchases consistent with the expected utility model?
Do insurers set premiums in competitive markets (without price regulation) so as to maximize expected profits?
RELEVANT ASSUMPTIONS FOR EXAMINING BEHAVIOR
Answers to these two questions are simplest in situations with well-specified and well-known loss probabilities and an insurance market that has the following characteristics:
A substantial number of at-risk individuals whose losses are independent of one another;
Loss amount per event that is large relative to buyers’ wealth but small relative to insurers’ capital;
Low costs to consumers for becoming well informed about potential losses;
Freedom of consumers to decide whether to buy insurance and, if so, how much coverage to purchase;
Free entry by insurers, with freedom to set premiums.