Investigating assumptions of vulnerability: A case study of the exclusion of psychiatric inpatients as participants in genetic research in low- and middle-income contexts.
Andrea C PalkMary A BittaEunice KamaaraDan J SteinIlina SinghPublished in: Developing world bioethics (2020)
Psychiatric genetic research investigates the genetic basis of psychiatric disorders with the aim of more effectively understanding, treating, or, ultimately, preventing such disorders. Given the challenges of recruiting research participants into such studies, the potential for long-term benefits of such research, and seemingly minimal risk, a strong claim could be made that all non-acute psychiatric inpatients, including forensic and involuntary patients, should be included in such research, provided they have capacity to consent. There are tensions, however, regarding the ethics of recruiting psychiatric inpatients into such studies. In this paper our intention is to elucidate the source of these tensions from the perspective of research ethics committee interests and decision-making. We begin by defining inpatient status and outline some of the assumptions surrounding the structures of inpatient care. We then introduce contemporary conceptions of vulnerability, including Florencia Luna's account of vulnerability which we use as a framework for our analysis. While psychiatric inpatients could be subject to consent-related vulnerabilities, we suggest that a particular kind of exploitation-related vulnerability comes to the fore in the context of our case study. Moreover, a subset of these ethical concerns takes on particular weight in the context of genetic research in low- and middle-income countries. At the same time, the automatic exclusion of inpatients from research elicits justice-related vulnerabilities.
Keyphrases
- mental health
- climate change
- genome wide
- decision making
- palliative care
- end stage renal disease
- public health
- copy number
- healthcare
- mental illness
- chronic kidney disease
- newly diagnosed
- physical activity
- big data
- body mass index
- high resolution
- peritoneal dialysis
- liver failure
- gene expression
- machine learning
- prognostic factors
- deep learning
- quality improvement
- drug induced
- patient reported outcomes
- dna methylation
- global health
- acute respiratory distress syndrome
- patient reported
- artificial intelligence