Social Science Research/Illnesses, Addictions, and Care for the Homeless

Social Science Research Illnesses, Addictions, and Care for the Homeless Rehabilitation of the Homeless Stigma Against Mental Illness on Social Networks The study https://pmc.ncbi.nlm.nih.gov/articles/PMC6651952/ titled "Making Sense of Street Chaos: An Ethnographic Exploration of Homeless People's Health Service Utilization": O'Carroll A, Wainwright D. Making Sense of Street Chaos: An Ethnographic Exploration of Homeless People's Health Service Utilization. Int J Equity Health. 2019 Jul 23;18(1):113. doi: 10.1186/s12939-019-1002-6. PMID: 31337407; PMCID: PMC6651952. explores the use of health services by homeless people in Dublin, revealing that, despite their poor health, they often access services late, discontinue treatment, and prefer emergency rooms to primary care. The dataset in Table 2 of the article contains demographic data and behavioral patterns from 47 semi-structured interviews (31 males, 16 females) with homeless people at three fixed sites in Dublin (a homeless day center, a soup kitchen, and a local emergency room). Therefore, using Bayesian inference, the findings can be generalized to homeless people in Dublin who attend similar services or who have similar demographic profiles and problems to the interviewed participants. Some variables in the table are: Hx IV Drug Use Hx Benzo Abuse Hx Alcohol Misuse Hx of Methadone Treatment Hx of being in care In the R programming language using RStudio, the bayes_inference function allows you to construct credible intervals, i.e., probabilistic intervals containing, for example, the true percentage of homeless people receiving healthcare. The first argument, y, specifies the response variable we're interested in: Hx.of.being.in.care. The second argument, data, specifies the dataset containing the variable Hx.of.being.in.care, which can take the values ​​Yes or No. The third argument, statistic, is the statistic we're using, i.e., proportion (percentage). The type argument specifies the type of inference we want: credible intervals ( type = "ci" ), or hypothesis testing ( type = "ht" ). The prior_family argument indicates which prior distribution we will use for inference, namely JZS (Zellner-Siow Cauchy). The function returns the credibility interval for which we can state that there is a 95% probability that homeless people in Dublin similar to the respondents and receiving care in the healthcare system fall within the credibility interval [6.07%, 25.25%], and the predicted posterior mean is only 14.29% library(statsr) bayes_inference(y = Hx.of.being.in.care, data = dati, statistic = "proportion", type = "ci", prior_family = "JZS", rscale = 1, method = "theoretical", success = "Y", cred_level = 0.95, beta_prior = c(1,1)) Single categorical variable, success: Y n = 47, p-hat = 0.1277 95% CI: (0.0607 , 0.2525) Post. mean = 0.1429 Post. median = 0.1379 Post. mode = 0.1277 Repeating the same analysis for drug use, benzodiazepine use, alcohol use, hepatitis C use, HIV use, and mental illness, the percentages are much higher: Drug use: 95% CI: [74.75%, 93.93%] Benzodiazepine use: 95% CI: [62.69%, 86.36%] Alcohol use: 95% CI: [13.64%, 37.31%] Mental illness: 95% CI: [39.17%, 66.71%] HIV: 95% CI: [6.07%, 25.25%] Hepatitis C: 95% CI: [31.37%, 58.82%] The article identifies several reasons why Irish homeless people tend to use emergency departments (EDs) more than primary care despite their poor health: Lack of access to primary care: Many homeless people report not visiting general practitioners (GPs) because they do not have a health card that entitles them to free care. Complexity of administrative procedures: Even when in possession of a health card, completing the forms required to access primary care can be a barrier. Use of the ED as a shelter: Some homeless people use the ED primarily as a safe and warm place to sleep, rather than to receive medical care. Assumption of not receiving adequate addiction treatment: Many participants assumed they would not receive medications such as methadone or Librium to manage drug or alcohol withdrawal symptoms in emergency rooms, leading them to avoid them or leave prematurely. This assumption can also prevent them from going to the emergency room even when they have serious health problems. Difficulty with appointments: Due to theGiven the chaotic nature of homelessness and the priority given to survival (obtaining food, drugs, shelter), keeping medical appointments becomes difficult. Participants reported not prioritizing healthcare appointments. Attitudinal barriers and stigma: Homeless people often experience stigma and discrimination from primary care providers, leading them to avoid them. Some reported being treated with disdain. Negative experiences with primary care physicians: Some participants had had negative experiences with their primary care physicians, such as being excluded from their patient lists. Lack of self-assertion skills: Homeless people may lack the self-assertion skills needed to navigate the primary care healthcare system and assert their needs. Prioritization of immediate survival: Immediate survival needs (such as finding food and shelter) often take precedence over personal health care.