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In four experiments covering three different life domains, participants made future predictions in what they considered the most realistic scenario, an optimistic best-case scenario, or a pessimistic worst-case scenario (N = 2,900 Americans). Consistent with a best-case heuristic, participants made \"realistic\" predictions that were much closer to their best-case scenario than to their worst-case scenario. We found the same best-case asymmetry in health-related predictions during the COVID-19 pandemic, for romantic relationships, and a future presidential election. In a fully between-subject design (Experiment 4), realistic and best-case predictions were practically identical, and they were naturally made faster than the worst-case predictions. At least in the current study domains, the findings suggest that people generate \"realistic\" predictions by leaning toward their best-case scenario and largely ignoring their worst-case scenario. Although political conservatism was correlated with lower covid-related risk perception and lower support of early public-health interventions, the best-case prediction heuristic was ideologically symmetric.

This JAMA Health Forum discusses challenges to the nation’s health and the case for optimism for better population health based on 3 pillars.

There is increasing interest in clinical prediction models for rare outcomes such as suicide, psychiatric hospitalizations, and opioid overdose. Accurate model validation is needed to guide model selection and decisions about whether and how prediction models should be used. Split-sample estimation and validation of clinical prediction models, in which data are divided into training and testing sets, may reduce predictive accuracy and precision of validation. Using all data for estimation and validation increases sample size for both procedures, but validation must account for overfitting, or optimism. Our study compared split-sample and entire-sample methods for estimating and validating a suicide prediction model.
We compared performance of random forest models estimated in a sample of 9,610,318 mental health visits (\"entire-sample\") and in a 50% subset (\"split-sample\") as evaluated in a prospective validation sample of 3,754,137 visits. We assessed optimism of three internal validation approaches: for the split-sample prediction model, validation in the held-out testing set and, for the entire-sample model, cross-validation and bootstrap optimism correction.
The split-sample and entire-sample prediction models showed similar prospective performance; the area under the curve, AUC, and 95% confidence interval was 0.81 (0.77-0.85) for both. Performance estimates evaluated in the testing set for the split-sample model (AUC = 0.85 [0.82-0.87]) and via cross-validation for the entire-sample model (AUC = 0.83 [0.81-0.85]) accurately reflected prospective performance. Validation of the entire-sample model with bootstrap optimism correction overestimated prospective performance (AUC = 0.88 [0.86-0.89]). Measures of classification accuracy, including sensitivity and positive predictive value at the 99th, 95th, 90th, and 75th percentiles of the risk score distribution, indicated similar conclusions: bootstrap optimism correction overestimated classification accuracy in the prospective validation set.
While previous literature demonstrated the validity of bootstrap optimism correction for parametric models in small samples, this approach did not accurately validate performance of a rare-event prediction model estimated with random forests in a large clinical dataset. Cross-validation of prediction models estimated with all available data provides accurate independent validation while maximizing sample size.

Polycystic ovary syndrome (PCOS) is a chronic endocrinopathy characterized by hyperandrogenism and anovulation that may pervade psychological dimensions such as dispositional optimism. Considering how PCOS influences mental health and the lack of studies on this matter, this research was aimed at assessing optimism and associated factors in PCOS. A case-control study of 156 patients with PCOS and 117 controls was conducted. All woman filled out the Life Orientation Test-Revised (LOT-R), a self-report questionnaire for measuring dispositional optimism. Medication, pain severity, gynecological, and sociodemographic information was also collected. Lower optimism was found in patients with PCOS compared to controls, even after covariate adjustment (LOT-R global scores: 14.1 vs. 15.9, p = 0.020). Our study provides evidence that a personality characteristic with important implications in illness prognosis may be affected in PCOS. We propose to assess dispositional optimism with the LOT-R scale in the gynecological appointment and tailor medical attention accordingly as a way to improve the comprehensive care of these patients within a multidisciplinary team.

Background and Aims: The COVID-19 pandemic has led to radical and unexpected changes in everyday life, and it is plausible that people\'s psychophysical health has been affected. This study examined the relationship between COVID-19 related knowledge and mental health in a Croatian sample of participants.
METHODS: An online survey was conducted from March 18 until March 23, 2020, and a total of 1244 participant responses were collected (85.5% were women and 58.4% completed secondary education). Measures included eight questions regarding biological features of the virus, symptoms, and prevention, the Hospital Anxiety and Depression Scale, and Optimism-Pessimism Scale. According to the answers given on the questions on COVID-19 related knowledge, participants were divided in two groups: (1) informed and (2) uninformed on each question. They were then compared in the expressed levels of anxiety, depression, pessimism, and optimism. Full vs. partial mediation models with optimism/pessimism as a mediator in the relationship between anxiety/depression and the accuracy of responses for questions about handwashing and ways of transmission were estimated.
RESULTS: Participants who responded correctly on the question about handwashing had higher levels of anxiety, depression, and pessimism than those participants whose answer was incorrect, while participants who answered correctly on the question about the percentage of patients who develop serious breathing problems had higher levels of depression than those who answered incorrectly. Lower levels of anxiety and pessimism were observed in the participants who answered correctly about ways of transmission. Higher levels of pessimism were found in participants who scored incorrectly on questions about the efficiency of antibiotics, most common symptoms, and the possibility of being infected by asymptomatic carriers. Higher levels of knowledge about handwashing were predicted by higher levels of anxiety and pessimism. Higher levels of knowledge about ways of transmission were predicted by lower levels of anxiety and lower levels of pessimism. The examined relationships between anxiety/depression and knowledge were mediated by pessimism.
CONCLUSIONS: The findings of this study suggest that knowledge about COVID-19 may be useful to reduce anxiety and depression, but it must be directed to the promotion of health behaviors and to the recognition of fake news.