Parent Civic Behavior and Observed Civic Messages: Associations with Adolescent Civic Behavior and Prioritization

The current study employed observational and multi-informant survey methodology to explore associations among parents\u27 civic behaviors, observed parent and adolescent messages about civic obligation, and adolescents\u27 civic behavior and prioritization (should) judgments. A sample of 160 adolescents (Mage = 14.42, range = 12-18) and their parents (144 mothers and 52 fathers), participated in video-recorded, structured, dyadic interaction tasks in which they discussed citizenship and civic duty. Parents and adolescents also completed questionnaires assessing civic behavior and civic prioritization judgments. Within distinct civic activities, parents\u27 report of civic behavior was positively associated with adolescents\u27 report of civic behavior and prioritization judgments. Over and above parents\u27 civic behavior, adolescents\u27 community service behavior was positively associated with parents\u27 observed messages about help and respect for others and one\u27s country but negatively associated with adolescents\u27 own observed messages about being productive members of society. Additionally, parents\u27 observed messages about the importance of following rules and regulations were negatively associated with their adolescents\u27 prioritization judgments concerning social movement involvement (e.g., protesting). Findings suggest that parents\u27 observed messages about citizenship and civic duty may promote and deter adolescents\u27 from engagement in specific civic activities

A longitudinal assessment of variability in COVID-19 vaccine hesitancy and psychosocial correlates in a national United States sample

National Science Foundation [BCS-2027027]This work was supported by a RAPID grant from the National Science Foundation under Award ID BCS-2027027. The funding organization was not involved in designing the study, collecting and analyzing the data, or preparing the manuscriptRecent evidence suggests that COVID-19 vaccine hesitancy is not static. In order to develop effective vaccine uptake interventions, we need to understand the extent to which vaccine hesitancy fluctuates and identify factors associated with both between- and within-person differences in vaccine hesitancy. The goals of the current study were to assess the extent to which COVID-19 vaccine hesitancy varied at an individual level across time and to determine whether disgust sensitivity and germ aversion were associated with between- and within-person differences in COVID-19 vaccine hesitancy. A national sample of U.S. adults (N = 1025; 516 woman; M-age = 46.34 years, SDage = 16.56, range: 18 to 85 years; 72.6 % White) completed six weekly online surveys (March 20 - May 3, 2020). Between-person mean COVID-19 vaccine hesitancy rates were relatively stable across the six-week period (range: 38-42 %). However, there was considerable within-person variability in COVID-19 vaccine hesitancy. Approximately, 40 % of the sample changed their vaccine hesitancy at least once during the six weeks. There was a significant between-person effect for disgust sensitivity, such that greater disgust sensitivity was associated with a lower likelihood of COVID-19 vaccine hesitance. There was also a significant within-person effect for germ aversion. Participants who experienced greater germ aversion for a given week relative to their own six week average were less likely to be COVID-19 vaccine hesitant that week relative to their own six-week average. This study provides important information on rapidly changing individual variability in COVID-19 vaccine hesitancy on a weekly basis, which should be taken into consideration with any efforts to decrease vaccine hesitancy and increase vaccine uptake. Further, these findings identify-two psychological factors (disgust sensitivity and germ aversion) with malleable components that could be leveraged in developing vaccine uptake interventions.WOS:0009467190000012-s2.0-8514785927036669969Science Citation Index ExpandedarticleUluslararası işbirliği ile yapılan - EVETŞubat2023YÖK - 2022-2

The Rise and Fall of Debris Disks: MIPS Observations of h and chi Persei and the Evolution of Mid-IR Emission from Planet Formation

We describe Spitzer/MIPS observations of the double cluster, h and $\chi$ Persei, covering a $\sim$ 0.6 square-degree area surrounding the cores of both clusters. The data are combined with IRAC and 2MASS data to investigate $\sim$ 616 sources from 1.25-24 $\mu m$. We use the long-baseline $K_{s}$-[24] color to identify two populations with IR excess indicative of circumstellar material: Be stars with 24 $\mu m$ excess from optically-thin free free emission and 17 fainter sources (J$\sim$ 14-15) with [24] excess consistent with a circumstellar disk. The frequency of IR excess for the fainter sources increases from 4.5 $\mu m$ through 24 $\mu m$. The IR excess is likely due to debris from the planet formation process. The wavelength-dependent behavior is consistent with an inside-out clearing of circumstellar disks. A comparison of the 24 $\mu m$ excess population in h and $\chi$ Per sources with results for other clusters shows that 24 $\mu m$ emission from debris disks 'rises' from 5 to 10 Myr, peaks at $\sim$ 10-15 Myr, and then 'falls' from $\sim$ 15/20 Myr to 1 Gyr.Comment: 48 pages, 15 figures, accepted for publication in The Astrophysical Journa

Self-quarantining, social distancing, and mental health during the COVID-19 pandemic: A multi wave, longitudinal investigation.

Social isolation and disconnectedness increase the risk of worse mental health, which might suggest that preventive health measures (i.e., self-quarantining, social distancing) negatively affect mental health. This longitudinal study examined relations of self-quarantining and social distancing with mental health during the COVID-19 pandemic. A U.S. national sample (N = 1,011) completed eight weekly online surveys from March 20, 2020 to May 17, 2020. Surveys assessed self-quarantining, social distancing, anxiety, and depression. Fixed-effect autoregressive cross-lagged models provided a good fit to the data, allowing for disaggregation of between-person and within-person effects. Significant between-person effects suggested those who engaged in more self-quarantining and social distancing had higher anxiety and depression compared to those who engaged in less social distancing and quarantining. Significant within-person effects indicated those who engaged in greater social distancing for a given week experienced higher anxiety and depression that week. However, there was no support for self-quarantining or social distancing as prospective predictors of mental health, or vice versa. Findings suggest a relationship between mental health and both self-quarantining and social distancing, but further longitudinal research is required to understand the prospective nature of this relationship and identify third variables that may explain these associations

Disease avoidance in the time of COVID-19: The behavioral immune system is associated with concern and preventative health behaviors.

The coronavirus disease 2019 (COVID-19) poses a serious global health threat. Without a vaccine, behavior change is the most effective means of reducing disease transmission. Identifying psychological factors that may encourage engagement in preventative health behaviors is crucial. The behavioral immune system (BIS) represents a set of psychological processes thought to promote health by encouraging disease avoidance behaviors. This study examined whether individual differences in BIS reactivity (germ aversion, pathogen disgust sensitivity) were associated with concern about COVID-19 and engagement in recommended preventative health behaviors (social distancing, handwashing, cleaning/disinfecting, avoiding touching face, wearing facemasks). From March 20 to 23, 2020, a US national sample (N = 1019) completed an online survey. Germ aversion and pathogen disgust sensitivity were the two variables most consistently associated with COVID-19 concern and preventative health behaviors, while accounting for demographic, health, and psychosocial covariates. Findings have implications for the development of interventions intended to increase preventative health behaviors

Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery

Background: Statistical models using machine learning (ML) have the potential for more accurate estimates of the probability of binary events than logistic regression. The present study used existing data sets from large musculoskeletal trauma trials to address the following study questions: (1) Do ML models produce better probability estimates than logistic regression models? (2) Are ML models influenced by different variables than logistic regression models? Methods: We created ML and logistic regression models that estimated the probability of a specific fracture (posterior malleolar involvement in distal spiral tibial shaft and ankle fractures, scaphoid fracture, and distal radial fracture) or adverse event (subsequent surgery [after distal biceps repair or tibial shaft fracture], surgical site infection, and postoperative delirium) using 9 data sets from published musculoskeletal trauma studies. Each data set was split into training (80%) and test (20%) subsets. Fivefold cross-validation of the training set was used to develop the ML models. The best-performing model was then assessed in the independent testing data. Performance was assessed by (1) discrimination (c-statistic), (2) calibration (slope and intercept), and (3) overall performance (Brier score). Results: The mean c-statistic was 0.01 higher for the logistic regression models compared with the best ML models for each data set (range, -0.01 to 0.06). There were fewer variables strongly associated with variation in the ML models, and many were dissimilar from those in the logistic regression models. Conclusions: The observation that ML models produce probability estimates comparable with logistic regression models for binary events in musculoskeletal trauma suggests that their benefit may be limited in this context

Feasibility of Machine Learning and Logistic Regression Algorithms to Predict Outcome in Orthopaedic Trauma Surgery

Background:Statistical models using machine learning (ML) have the potential for more accurate estimates of the probability of binary events than logistic regression. The present study used existing data sets from large musculoskeletal trauma trials to address the following study questions: (1) Do ML models produce better probability estimates than logistic regression models? (2) Are ML models influenced by different variables than logistic regression models?Methods:We created ML and logistic regression models that estimated the probability of a specific fracture (posterior malleolar involvement in distal spiral tibial shaft and ankle fractures, scaphoid fracture, and distal radial fracture) or adverse event (subsequent surgery [after distal biceps repair or tibial shaft fracture], surgical site infection, and postoperative delirium) using 9 data sets from published musculoskeletal trauma studies. Each data set was split into training (80%) and test (20%) subsets. Fivefold cross-validation of the training set was used to develop the ML models. The best-performing model was then assessed in the independent testing data. Performance was assessed by (1) discrimination (c-statistic), (2) calibration (slope and intercept), and (3) overall performance (Brier score).Results:The mean c-statistic was 0.01 higher for the logistic regression models compared with the best ML models for each data set (range, -0.01 to 0.06). There were fewer variables strongly associated with variation in the ML models, and many were dissimilar from those in the logistic regression models.Conclusions:The observation that ML models produce probability estimates comparable with logistic regression models for binary events in musculoskeletal trauma suggests that their benefit may be limited in this context