Using football cultures as a vehicle to improve mental health in men: the case of the Redcar and cleveland boot room

This paper sets out to appraise (from the perspective of members) the impact of a localized, football-based mental health intervention. Commissioned in late 2015, the ‘Redcar and Cleveland Boot Room (BR)’ was implemented in response to mass redundancy in the local area, coupled with regional suicide rates in men that exceed the national average. Interactive discussions with BR members revealed that: (a) the language of football and shared identity were important for initiating and sustaining engagement in the BR; (b) peer-support and mentoring combined with member-led activities were active ingredients of the BR and (c) that the BR was an effective vehicle for building mental health resilience. This evaluation adds to the evidence base on the value of football as a context to engage adult males in community-based interventions targeting mental health resilience.     

How accurate are gender detection tools in predicting the gender for Chinese names? A study with 20,000 given names in Pinyin format

Objective:We recently showed that the gender detection tools NamSor, Gender API, and Wiki-Gendersort accurately predicted the gender of individuals with Western given names. Here, we aimed to evaluate the performance of these tools with Chinese given names in Pinyin format.Methods:We constructed two datasets for the purpose of the study. File #1 was created by randomly drawing 20,000 names from a gender-labeled database of 52,414 Chinese given names in Pinyin format. File #2, which contained 9,077 names, was created by removing from File #1 all unisex names that we were able to identify (i.e., those that were listed in the database as both male and female names). We recorded for both files the number of correct classifications (correct gender assigned to a name), misclassifications (wrong gender assigned to a name), and nonclassifications (no gender assigned). We then calculated the proportion of misclassifications and nonclassifications (errorCoded).Results:For File #1, errorCoded was 53% for NamSor, 65% for Gender API, and 90% for Wiki-Gendersort. For File #2, errorCoded was 43% for NamSor, 66% for Gender API, and 94% for Wiki-Gendersort.Conclusion:We found that all three gender detection tools inaccurately predicted the gender of individuals with Chinese given names in Pinyin format and therefore should not be used in this population.