Healthy, resilient and flexible: Co-developing a mental health intervention for and with frontline gig workers: a systems approach

One thing the COVID-19 pandemic has highlighted is the essential role of frontline gig workers - such as couriers, taxi drivers and home carers - in keeping the economies and our lives going, even when everything else has come to a halt. However, many of them work in sectors that are increasingly integrated into the gig economy which tends to categorise workers as self-employed and platforms as technology companies, and hence the latter bear no employer responsibilities. This inevitably leads to insufficient health and safety support for these workers. This is a worrying concern as latest research has revealed that road accidents and reckless driving behaviour are common among those who drive in the gig economy. Systematic reviews about burnout among couriers and carers also revealed high level of burnout and a range of technology associated risk factors. Although the benefits of standardised work pieces on flexible terms is real for some, analysis of large scale surveys shed additional light on the variegated gig worker population. A major pattern in the results suggested that dependency on the platform as a main income source and personal financial strain can exacerbate the mental health penalties of platform work. That is to say, those who struggle financially or with a background which does not afford them more secured employment are more vulnerable to the exploitative practices of the platforms. Hence, the job that frontline gig workers do is in fact high demand, low control, and with minimal support, all of which contribute to job burnout. As the gig economy continues to expand, job burnout has become a major concern which could further affect workers' physical and mental health, safety and wellbeing. Currently, intervention research that targets frontline gig workers and aims to reduce job burnout is scarce. Hence, we propose to fill the gap and develop a system-level digital intervention that will interfere with and interrupt the algorithm of platform systems, in order to prevent excessive work stress. This will be collaboratively developed with key stakeholders, including the workers, platform providers and technology developers, experts and policy makers. We will also actively engage the co-op platforms to co-design the intervention and assess its feasibility. If this is proven feasible within this proposal, we will further engage with this sector and secure more resources to implement it on a larger scale.

Profit-driven platforms analyse the data flow and add features on the platforms to motivate workers (i.e. set up quests or inert a points system) or increase customer satisfaction (i.e. driver tracking or customer rating) or improve operational efficiency (i.e. demand response in minutes). These features and their logic of design can contribute to a stressful work environment and cause safety and health concerns. Latest research has revealed traffic rules violations and high rates of road accidents among workers who drive in the gig economy. The ultimate goal of this research is to develop a system-level digital intervention (main study) to prevent excessive work stress and reduce job burnout among frontline gig workers. It is envisaged as a set of intervention approaches and supporting mechanisms embedded in software architecture solutions that can be developed and integrated into existing platforms. It will influence, interrupt or modify the workflow and operation system that are controlled by the algorithm and system engineering logic. Ideally, it should be able to detect excessive workload, stressful moments and risky behaviours and inform the workers. This way, workers could gain more control by, for example, adjusting rate of order dispatch or delivery time window, negotiating performance incentive design or disputing bad customer reviews. Additional support functions can be added to offer more work-related support, such as a virtual line-manager or workers forum. To progress to the main study, it is extremely important that we use this PHIND stage to develop a technology model, identify desired functions and working mechanisms, working collaboratively and closely with all the key stakeholders. Based on the learnings, we can then design and implement a comprehensive intervention program and evaluate the effectiveness in the main study. The co-development will be guided by a theoretical model: job demand-resource (JD-R) model.