The landscape in which employers and candidates interact is changing as more job adverts are pushed online. Employment platforms (e.g., Indeed and LinkedIn) are now among the primary mechanisms for job posting, job search, and initial negotiations. Through such job platforms, a single job advert can now reach millions of people around the world. This exposure of a job advert has obvious benefits for the employer, but this exposure also has the power to alienate and exclude large portions of society. In particular, the word choice of a single job advert can, perhaps unintentionally, exclude thousands of people by their personal traits (e.g., gender or race). Age is a particular trait that garners more attention as ageism is often cited in the literature as going overlooked, not understood, and generally escaping social awareness. To begin tackling this problem, with the purpose of supporting older adults and enabling their contribution to society, we applied advances in AI to create a tool, called Exclusion Spotter, that gives feedback to recruiters and employers on which words in their advert are possibly excluding people by age. We applied Exclusion Spotter to 3660 job adverts, clustered by 372 job titles. We found a significant difference (p=.02) in the number of age-related words for engineering related positions versus all other job titles. Among 47 engineering related titles we matched 47.37 age related words per title and 2.8 per advert. Among the other 325 titles we matched 24.37 age related words per title and 2.1 per advert.
The urban-rural dichotomy underpins the common approach in studying environmental conditions influencing older adults’ lives characterized by post-Second World War urban migration in both Italy and the United States (US). However, the traditional opposition urban-rural dichotomy is inadequate to study how the environmental characteristics of a geographical area can account for the heterogeneous profile of its populations and its age distribution. This study aims to overcome the traditional mobility theories as an explanatory dichotomy for understanding the distribution of the age structure of a given population. The extent of a supportive network, and the connection between the place of residence to the proximity of other residential centres can be seen as potential resources for understanding the attractiveness of certain areas for older adults. A large harmonized set of demographic and socio-economic data were collected from the Italian National Institute of Statistics (ISTAT) and the American Community Survey (ACS). An analysis at the Italian municipality- and US county-level finds the population over 75 years old are overrepresented in rural areas of both countries as would be expected by available employment opportunities, but considerable heterogeneity among both urban and rural areas exist. In particular, rural more than urban settings are based on an informal support network that is argued to rely on human proximity to produce successful aging in the community. The variation in population of older adults in rural areas, therefore, might have implications on how to achieve age-friendly communities, aside from population’s traditional mobility theories and formal support network.
The impact of COVID-19 on older adults has been analysed through different research approaches. However, with its sudden global spread, combined with uncertainty about which countermeasures would be employed, there was a lack of opportunity to systematically and continuously engage in a system of observing the moods of older adults forced to live in unexpected conditions. Ageist narratives, social distancing, the unending barrage of real and fake news, and the lockdowns, have given rise to what we define as a series of “seasons” of life, characterised not by the weather barometer, but by moods of people. How much did these external events, like the impact of weather, affect the mood of older adults? We immediately recognised the pandemic’s long-term nature, and thanks to our position as an "observatory" of social dynamics, and because of our existing community of older adults (VOICE), we could involve our members to provide valuable insights about mood and wellbeing during the pandemic. We initiated a weekly pulse survey, based on the two same questions, starting in week 13 of 2020. Across the 50 weeks which followed, we received 2577 responses. They rated their mood on a scale of 1 (extra-stormy) to 5 (all sunshine), before we collated the data and mapped on key events related to media announcements and political decisions. Our research showed the impact of these events on the mood of participants, and the potential of this approach to identify trends in mood to help policy makers with informed decision-making during unprecedented times.
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