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An information technology adjunct to case administration in frontline social work: a randomised controlled trial on outcomes for clients

Project Description

Supervisors; Dr Tony McGinn, Prof Brian Taylor, Dr Johnathan Synnott, Dr Joanne Quinn

We asked our partners, in the world of social work practice, to suggest a research topic which had the potential to assist frontline social work staff. A dominant theme from this consultation was that the weight of administration duties puts pressure on client contact times. They described an interest in the evaluation of potential technological adjuncts, or substitutions, to the current administrative practices of front-line social work staff.


Following a comprehensive literature review and stakeholder consultation (encompassing benchmarking consultations) the successful candidate would be supported to review the potential of up to three software applications which can deliver voice recording and filing functionality in a public service setting. It is likely that these applications will include speech-to-text technologies, voice recording technologies and project/time-management applications. With stakeholder input, one of these technologies would be chosen for a comprehensive evaluation in a randomised controlled trial. Social work service users’ feedback, on the quality of relationship they enjoyed with their social worker, will be a key outcome of interest.


Applicants must apply using the online form on the University Alliance website at Full details of the programme, eligibility details and a list of available research projects can be seen at

The final deadline for application is 12 April 2019.

Funding Notes

DTA3/COFUND participants will be employed for 36 months with a minimum salary of (approximately) £20,989 per annum. Tuition fees will waived for DTA3/COFUND participants who will also be able to access an annual DTA elective bursary to enable attendance at DTA training events and interact with colleagues across the Doctoral Training Alliance(s).

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801604.

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