Can Self-affirmation Encourage HIV-Prevention? Evidence from Female Sex Workers in Senegal

We test an intervention aiming to increase condom usage and HIV testing in a stigmatized population at high risk of contracting HIV: female sex workers (FSWs) in Senegal. Some sex work is legal in Senegal, and condoms and HIV tests are freely available to registered FSWs-but FSWs may be reluctant to get tested and use condoms, in part because doing so would entail acknowledging their risk of contracting HIV and potentially expose them to stigma. Drawing on self-affirmation theory, we hypothesized that reflecting on a source of personal pride would help participants acknowledge their risk of HIV, intend to use condoms more frequently, and take an HIV test. Prior research suggests that similar self-affirmation interventions can help people acknowledge their health risks and improve their health behavior, especially when paired with information about effectively managing their health (i.e., self-efficacy information). However, such interventions have primarily been tested in the United States and United Kingdom, and their generalizability outside of these contexts is unclear. Our high-powered experiment randomly assigned participants (N = 592 FSWs; N = 563 in the final analysis) to a self-affirmation condition or a control condition and measured their risk perceptions, whether they took condoms offered to them, and whether (after randomly receiving or not receiving self-efficacy information) they took an HIV test. We found no support for any of our hypotheses. We discuss several explanations for these null results based on the stigma attached to sex work and HIV, cross-cultural generalizability of self-affirmation interventions, and robustness of previous findings

Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival. METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set. CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome

When virtue leads to villainy: advances in research on moral self-licensing

Acting virtuously can subsequently free people to act less-than-virtuously. We review recent insights into this moral selflicensing effect: first, it is reliable, though modestly sized, and occurs in both real-world and laboratory contexts; second, planning to do good, reflecting on foregone bad deeds, or observing ingroup members' good deeds is sufficient to license less virtuous behavior; third, when people need a license, they can create one by strategically acting or planning to act more virtuously, exaggerating the sinfulness of foregone bad deeds, or reinterpreting past behavior as moral credentials; and fourth, moral self-licensing effects seem most likely to occur when people interpret their virtuous behavior as demonstrating their lack of immorality but not signaling that morality is a core part of their self-concept

Moral credentials and the 2020 democratic presidential primary: no evidence that endorsing female candidates licenses people to favor men

Endorsing Obama in 2008 licensed some Americans to favor Whites over Blacks––an example of moral self-licensing (Effron, Cameron, & Monin, 2009). Could endorsing a female presidential candidate in 2020–21 similarly license Americans to favor men at the expense of women? Two high-powered, pre-registered experiments found no evidence for this possibility. We manipulated whether Democrat participants had an opportunity to endorse a female Democratic candidate if she ran against a male candidate (i.e., Trump in Study 1, N = 2143; an anti-Trump Republican or independent candidate in Study 2, N = 2228). Then, participants read about a stereotypically masculine job and indicated whether they thought a man should fill it. Contrary to predictions, we found that endorsing a female Democrat did not increase participants' tendency to favor men over women for the job. We discuss implications for the robustness and generalizability of moral self-licensing