ABSTRACT
NoFap is a community of mostly heterosexual men abstaining from what they see as an addictive cycle of pornography, masturbation, and orgasm, induced by the exploitation of innate male urges by the pornography industry. In the general population, increased masturbation and consumption of pornography are associated with psychological factors including low affect, loneliness, and boredom, all of which may be exacerbated by the lockdown/social distancing measures adopted in response to the Covid-19 pandemic. The present study explores how the NoFap community has responded to the pandemic through discourse analysis of an online message board. We identify four key themes: i) I let go in lockdown, ii) the opportunity of lockdown, iii) testing the parameters of NoFap, and iv) community cohesion. Each is defined and discussed. Combined they illustrate a digital community struggling to honor its meritocratic masculine ideals in the face of challenging circumstances. [ FROM AUTHOR] Copyright of Sexualities is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
SARS-CoV-19 PCR testing has a turn-around time that makes it impractical for real-time decision-making, and current point-of-care tests have limited sensitivity, with frequent false negatives. The study objective was to develop a clinical prediction rule to use with a point-of-care test to diagnose COVID-19 in symptomatic outpatients. A standardized clinical questionnaire was administered prior to SARS-CoV-2 PCR testing. Data was extracted by a physician blinded to the result status. Individual symptoms were combined into 326 unique clinical phenotypes. Multivariable logistic regression was used to identify independent predictors of COVID-19, from which a weighted clinical prediction rule was developed, to yield stratified likelihood ratios for varying scores. A retrospective cohort of 120 SARS-CoV-2-positive cases and 120 SARS-CoV-2-negative matched controls among symptomatic outpatients in a Connecticut HMO was used for rule development. A temporally distinct cohort of 40 cases was identified for validation of the rule. Clinical phenotypes independently associated with COVID-19 by multivariable logistic regression include loss of taste or smell (olfactory phenotype, 2 points) and fever and cough (febrile respiratory phenotype, 1 point). Wheeze or chest tightness (reactive airways phenotype, - 1 point) predicted non-COVID-19 respiratory viral infection. The AUC of the model was 0.736 (0.674-0.798). Application of a weighted C19 rule yielded likelihood ratios for COVID-19 diagnosis for varying scores ranging from LR 15.0 for 3 points to LR 0.1 for - 1 point. Using a Bayesian diagnostic approach, combining community prevalence with the evidence-based C19 rule to adjust pretest probability, clinicians can apply a point of care test with limited sensitivity across a range of clinical scenarios to differentiate COVID-19 infection from influenza and respiratory viral infection.