Search results

This study aims to explore homeless-support workers’ perceptions of homeless welfare recipients and their experiences of navigating new conditions placed upon them by UK welfare reform. It examines support workers’ views of the most punitive feature of the welfare system, sanctions, on those recipients.In 2012, the Conservative and Liberal Democrat Coalition Government introduced the largest and most radical overhaul of the UK benefit system, significantly increasing the level of conditionality and sanctions for non-compliance, part of a shift in welfare, suggesting that rights must be balanced by responsibility and the “culture of worklessness” and “benefit dependency” should be addressed.

Welfare reforms in the UK and the increased use of sanctions as part of welfare conditionality are reviewed. Data are collected from eight semi-structured interviews taking place in five housing support groups in the South East and South West of England in 2019–2020. The interviews followed an approach from interpretive phenomenological analysis.

Findings from this study indicate that the government’s reforms serve as a disciplinary measure for the poor, reinforcing inequality and social marginalization. To mitigate the effects would require a comprehensive review of universal credit prior to its full rollout to claimants. Data are analyzed thematically.

Welfare conditionality and welfare reform is well-researched in the UK. There is also a significant volume of research concerning homelessness. This paper, however, fills a gap in research concerning the experiences of those working in housing support agencies working with homeless people in the UK.

This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.

This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.

This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.

This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.