Government policy on extending working lives will require people to work longer due to ageing demographics and fiscal challenges around pension availability. Workforce crises are observed in Health and Social Care sectors and the NHS with growing concerns about workplace mental health and early retirement of health and social care staff on health grounds (CMO 2015; CIPD 2018). Innovative methods including technological solutions can provide interventions with specific populations that need to be firstly developed and then tested. Digital interventions (DIs) are any intervention that is accessed on a computer or mobile phone. DIs have been created to help with, for example, weight loss, smoking, anxiety management and other mental health issues. Artificial intelligence chatbots are conversational agents that facilitate conversation between a human and a computer using learning capabilities.
Chatbot applications are an attractive proposition for human resource interventions and mental health care as they can provide cost efficient time and location independent guidance and counselling without long waiting times. Chatbots for personal and sensitive issues and health and well-being are often considered favourable in terms of privacy, ‘impression management’ and anonymity that prevents stigmatisation and judgement. Presently, chatbots can complete semantic analysis of the text that the user inputs, to provide a more tailored response.
Chatbots offer polylingual, timely independent guidance and counselling to those suffering from anxiety, nervousness, isolation, sadness or sleeping disorders. This technology can be tailored to job related issues, well-being at work, career and retirement planning. Whilst chatbots rely on words only and can attempt to track mood, non-verbal communication like emotion, body language and eye contact are omitted and these are often of key interest in establishing and dealing with mental health issues. Emotion Recognition technology, has the capacity to detect low mood, anxiety or mental health concerns using facial emotion recognition, eye tracking and machine learning approaches. Quantitative and objective diagnosis support engagement of people, examining their emotional responses and reflecting on different psychological conditions.
This study aims to use knowledge and understanding on issues related to workforce ageing, health and wellbeing concerns and intention to leave work at varying stages of one’s career to develop a ‘Job-Bot’ Digital Intervention to provide a sustainable intervention to support individuals to monitor health and well-being, and provide career long advice, guidance and counselling on health and well-being at work and options to healthily extend working life.
(1)To analyse existing relevant demographic information, attitudes to ageing at work and self-reporting planning around retirement, intention and plans to leave work or extend working life, from cross sectional data from social workers (McFadden 2018), nurses and midwives in the UK (Gillen and McFadden, ongoing);
(2) To develop intellectual capital to inform the production of ‘Job-Bot’, a sustainable product to support individual workers to enhance well-being at work and exercise informed choice about how to healthily extend working lives;
(3) To lead to publications in esteemed journals on the production of Digital Technology for workforce intervention with evidence to inform extending (PfG) government working lives
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 14,777 per annum for three years. EU applicants will only be eligible for the fees component of the studentship (no maintenance award is provided). For Non EU nationals the candidate must be "settled" in the UK.
Monday 18 February 2019
25 to 27 March 2019
A key player in the economy of the north west
Monday 25 November 2019
Computer Science and Informatics