Fulfilling Lives: Supporting People Experiencing Multiple Disadvantage, 2014–2022

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The Fulfilling Lives programme was one of a series of strategic investments made by The National Lottery Community Fund that were intended to make progress on complex social issues by providing substantial funding over an extended period in targeted parts of the country. The Fund invested £112 million over 8 years (2014-2022) to support adults experiencing multiple disadvantage. Multiple disadvantage was defined by the programme as experience of two or more of homelessness, substance misuse, offending and mental ill-health. The data in this study cover 2014-2021.

The programme funded voluntary sector-led partnerships in 12 areas across England: Birmingham; Blackpool; Bristol; Brighton and Hove, Eastbourne and Hastings; Camden and Islington; Lambeth, Lewisham and Southwark; Liverpool; Manchester; Newcastle and Gateshead; Nottingham; Stoke-on-Trent; and West Yorkshire. Target areas were selected with high volumes of people experiencing multiple disadvantage.

The Fund commissioned CFE Research (CFE) and The University of Sheffield in 2013 to carry out a national evaluation of the programme. The Fund's brief included a requirement for a common measurement framework and tools to ensure the collection of comparable data on people supported by partnerships. The resulting quantitative data is a key source of information about individual-level outcomes, characteristics and the support and services they used.

The data collected includes four main types of information:

• data on the individual beneficiary and their engagement with the Fulfilling Lives programme - beneficiary record
• an assessment by support staff of beneficiary's levels of need and risk - New Directions Team Assessment score
• a joint assessment between support staff and the beneficiary of their self-reliance in key areas - Homelessness Outcomes Star scores
• data on beneficiary interactions with key public services - service use data.

Tracking variables were updated quarterly. All types of data are linked in a single longitudinal dataset.