Perceived Stigma of Patients Undergoing Treatment with Cannabis-Based Medicinal Products.
Lucy J TroupSimon ErridgeBeata CieslukMikael H SodergrenPublished in: International journal of environmental research and public health (2022)
Cannabis-based medicinal products (CBMPs) are prescribed with increasing frequency. This study aimed to investigate the perceived stigma attached to patients prescribed CBMPs in the UK to establish its prevalence. A qualitative survey was developed by an expert multidisciplinary group and data were collected via Qualtrics. In total, 2319 patients on CBMP therapy were invited to take part in this study. 450 (19.4%) participants completed the questionnaire. In total, 81.3% ( n = 366), 76.9% ( n = 346), and 61.3% ( n = 276) of participants reported feeling very comfortable or comfortable telling friends, family, and medical professionals, respectively, about their treatment. Participants thought that friends ( n = 372; 82.7%) and family ( n = 339; 75.3%) were very approving or somewhat approving of their CBMP prescription. However, participants thought that only 37.8% ( n = 170) of healthcare professionals and 32.9% ( n = 148) of society in general were very approving or somewhat approving of their CBMP prescription. 57.1% ( n = 257), 55.3% ( n = 249), and 40.2% ( n = 181) of participants were afraid of what the police or criminal justice system, other government agencies, and healthcare professionals might think about their treatment. This study highlights those patients treated with CBMPs experience a high prevalence of perceived stigma from many corners of society. Future work should be undertaken to explore strategies to reduce perceived stigma at an individual and community level to avoid discrimination of patients, likely increasing appropriate access.
Keyphrases
- mental health
- social support
- end stage renal disease
- ejection fraction
- chronic kidney disease
- depressive symptoms
- healthcare
- patients undergoing
- physical activity
- peritoneal dialysis
- hiv aids
- cross sectional
- patient reported outcomes
- risk factors
- hepatitis c virus
- deep learning
- replacement therapy
- hiv infected
- data analysis