Screening of Fixed Prosthodontic Dentures after Five Years of Use in Relation to Material and Construction

The aim of this study was to screen patients with fixed prosthodontic appliances that were in oral cavity for a period of 5 years or more and to assess clinically and radiologically root caries, gingival recession, pocket formation, alveolar ridge resorption, as well as gingival (GI) and plaque index (PI). The aim also was to find out the differences between materials and constructions, between abutment and non-abutment teeth, and to find out the need for replacement. A total of 260 patients and their orthopantomograms were examined, with a total of 2,265 teeth, 610 being bridge abutments and 246 being crowns. The most frequent were metal+ acrylic veneer crowns or bridges. Root caries was found under the abutments in 10–20%; however abutments with ceramic crowns had the lowest percentage of caries (p<0.01). Alveolar ridge resorption, pocket formation deeper than 3 mm and gingival recession of various degree was found in 50% of the cases, again with the lowest percentage of ceramic-fused-to-metal appliances (p<0.01). Pocket depth was registered in significantly higher percentage in metal-acrylic veneer appliances compared to natural teeth (p<0.01), while there was no significant difference between metal-ceramic appliances and natural teeth (p>0.05). Although the worst findings were recorded for metal-+acrylic veneer crowns for PI, no significant difference existed between crowns of different material or non-abutment teeth (p>0.05). There was statistically significant difference between abutments with metal + acrylic veneer crowns, full metal crowns, metal ceramic crowns and non-abutments for GI scores. Higher percentage of scores 0 and 1 was recorded for metal ceramic crowns and non-abutments and significantly higher percentage of scores 2 and 3 was recorded for metal + acrylic veneer crowns and full metallic crowns. Almost 50% of metal-ceramic abutments had no pathologic findings. Almost 30% of the patients needed replacement, or even some abutments to be extracted and therefore a new prosthodontic appliance

Lives versus Livelihoods? Perceived economic risk has a stronger association with support for COVID-19 preventive measures than perceived health risk

This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive

Concern with COVID-19 pandemic threat and attitudes towards immigrants: The mediating effect of the desire for tightness

Tightening social norms is thought to be adaptive for dealing with collective threat yet it may have negative consequences for increasing prejudice. The present research investigated the role of desire for cultural tightness, triggered by the COVID-19 pandemic, in increasing negative attitudes towards immigrants. We used participant-level data from 41 countries (N = 55,015) collected as part of the PsyCorona project, a crossnational longitudinal study on responses to COVID-19. Our predictions were tested through multilevel and SEM models, treating participants as nested within countries. Results showed that people’s concern with COVID19 threat was related to greater desire for tightness which, in turn, was linked to more negative attitudes towards immigrants. These findings were followed up with a longitudinal model (N = 2,349) which also showed that people’s heightened concern with COVID-19 in an earlier stage of the pandemic was associated with an increase in their desire for tightness and negative attitudes towards immigrants later in time. Our findings offer insight into the trade-offs that tightening social norms under collective threat has for human groups

Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis

Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies

Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

The Precarity of Progress: Implications of a Shifting Gendered Division of Labor for Relationships and Well-Being as a Function of Country-Level Gender Equality

The onset of the COVID-19 pandemic saw a shift toward a more traditional division of labor–one where women took greater responsibility for household tasks and childcare than men. We tested whether this regressive shift was more acutely perceived and experienced by women in countries with greater gender equality. Cross-cultural longitudinal survey data for women and men (N = 10,238) was collected weekly during the first few months of the pandemic. Multilevel modelling analyses, based on seven waves of data collection, indicated that a regressive shift was broadly perceived but not uniformly felt. Women and men alike perceived a shift toward a more traditional division of household labor during the first few weeks of the pandemic. However, this perception only undermined women’s satisfaction with their personal relationships and subjective mental health if they lived in countries with higher levels of economic gender equality. Among women in countries with lower levels of economic gender equality, the perceived shift predicted higher relationship satisfaction and mental health. There were no such effects among men. Taken together, our results suggest that subjective perceptions of disempowerment, and the gender role norms that underpin them, should be considered when examining the gendered impact of global crisis

Trust in government regarding COVID-19 and its associations with preventive health behaviour and prosocial behaviour during the pandemic: a cross-sectional and longitudinal study

Background. The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust ingovernment regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government duringthe pandemic.Methods. This study analysed data from the PsyCorona Survey, an international project onCOVID-19 that included 23 733 participants from 23 countries (representative in age andgender distributions by country) at baseline survey and 7785 participants who also completedfollow-up surveys. Specification curve analysis was used to examine concurrent associationsbetween trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes.Results. Higher trust in government regarding COVID-19 control was significantly associatedwith higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β =0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated withtrust in government (standardised β = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trustat baseline survey was significantly associated with lower rate of decline in health behavioursover time ( p for interaction = 0.001).Conclusions. These results highlighted the importance of trust in government in the control of Covid-19

.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

‘We are all in the same boat’ : how societal discontent affects intention to help during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic has caused a global health crisis. Consequently, many countries have adopted restrictive measures that caused a substantial change in society. Within this framework, it is reasonable to suppose that a sentiment of societal discontent, defined as generalized concern about the precarious state of society, has arisen. Literature shows that collectively experienced situations can motivate people to help each other. Since societal discontent is conceptualized as a collective phenomenon, we argue that it could influence intention to help others, particularly those who suffer from coronavirus. Thus, in the present study, we aimed (a) to explore the relationship between societal discontent and intention to help at the individual level and (b) to investigate a possible moderating effect of societal discontent at the country level on this relationship. To fulfil our purposes, we used data collected in 42 countries (N = 61,734) from the PsyCorona Survey, a cross-national longitudinal study. Results of multilevel analysis showed that, when societal discontent is experienced by the entire community, individuals dissatisfied with society are more prone to help others. Testing the model with longitudinal data (N = 3,817) confirmed our results. Implications for those findings are discussed in relation to crisis management. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant