Categorising Coercion in Assisted Dying. An AI-Assisted Analysis of the House of Commons Debate on the 2025 Terminally Ill Adults Bill in England and Wales
Hamarat, N.; Gonzalez-Hijon, J.; Wels, J.
Show abstract
BackgroundThe debate on voluntary assisted dying (VAD) in England and Wales has long centred on safeguarding autonomy and preventing coercion. MethodsThis study analyses parliamentary debates (2024-2025) on the Terminally Ill Adults (End of Life) Bill to explore how Members of Parliament (MPs) framed coercion risks in assisted dying legislation. Using AI-assisted text analysis of Hansard transcripts, we identify seven coercion types: economic (financial pressure), ethnic (healthcare inequalities), familial (family influence), medical (clinician bias), mental (psychiatric conditions), poor care (inadequate palliative services), and self-coercion (internalised pressure). ResultsThese concerns were transpartisan, with no party disproportionately emphasising any single coercion type. Poor care and mental coercion were most frequently cited, reflecting concerns over UK healthcare underfunding and psychological vulnerability. Network analysis revealed strong co-occurrence between medical and mental coercion, as well as familial and economic pressures, suggesting MPs viewed these as interconnected risks. However, debates largely focused on individual-level safeguards (e.g., capacity assessments) rather than systemic monitoring of structural inequalities. ConclusionsWe argue that effective VAD regulation requires a dual approach: individual protections alongside population-level oversight to address disparities in access and prevent coercion across socioeconomic and ethnic groups. This framework could inform future legislation in the UK and other jurisdictions considering assisted dying reforms.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Intentional and unintentional non-adherence to social distancing measures during COVID-19: A mixed-methods analysis 94%
- Bereavement care for ethnic minority communities: A systematic review of access to, models of, outcomes from, and satisfaction with, service provision 94%
- The impact of COVID-19 non-pharmaceutical interventions on the lived experiences of people living in Thailand, Malaysia, Italy and the United Kingdom: a cross-country qualitative study 93%
Similar papers in this journal
- Challenges to self-isolation among contacts of cases of COVID-19: a national telephone survey in Wales 93%
- Social relationships and activities following elimination of SARS-CoV-2: a qualitative cross-sectional study 92%
- Understanding patterns of adherence to COVID-19 mitigation measures: A qualitative interview study 91%
Similar papers in this journal
- Which factors should be included in triage? An online survey of the attitudes of the UK general public to pandemic triage dilemmas 94%
- Adverse event reviews in healthcare: What matters to patients and their family? A qualitative study exploring the perspective of patients and family 94%
- Impact of COVID-19 pandemic on Black, Asian and Minority Ethnic (BAME) communities: a qualitative study on the perspectives of BAME community leaders 93%
Similar papers in this journal
- Cracking the Code: A Scoping Review to Unite Disciplines in Tackling Legal Issues in Health Artificial Intelligence 92%
- AI-Generated Clinical Summaries: Errors and Susceptibility to Speech and Speaker Variability 92%
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey 92%
Similar papers in this journal
- Following the science? Views from scientists on government advisory boards during the COVID-19 pandemic: a qualitative interview study in five European countries 94%
- Rethinking Paediatric Sepsis Care through Local Provider Voices and Lived Systems: A Mixed-Methods Study in Two Hospitals in Ghana 91%
- Voices from the frontline: findings from a thematic analysis of a rapid online global survey of maternal and newborn health professionals facing the COVID-19 pandemic 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.