Julia Parker
Unit 3 Treatments
Moving into unit 3 treatments were discussed. Looking at the graph below it can be shown what exactly the treatments were. Some seem extreme, which they were. Because some were so extreme, they may have caused more harm than good. The stigma continues especially when the treatment protocol was shared with the public. Although some were harsh, some experienced positive results.
Some examples of treatments include the tranquilizer chair and rotational therapy. The tranquilizer chair’s purpose was to limit sensory output and stop movement. Rotational therapy served the purpose of resetting the nervous system, meaning the rotation would cause the individual to let out all their bodily fluids. This was used as a disciplinary role. Interestingly, this type of treatment influenced roller coasters and amusement parks. But the difference is amusement park rides are voluntary and people chose to go on them. Therapy wise, the patients did not always have a say in the therapy.
Philippe Pinel was vital during the late 1700s to early 1800s because because he stood for humane treatment. Using arbitrary distributions and the patients using their own judgment. Today, it is looked at as being moral. Being humane and moral entailed better results. There are many parts in institutional history that are dark and obscure because of the harsh and inhumane treatment some were faced with. Pinel was against being cruel and harsh. Pinel’s methods definitely have inspired today’s look at mental health and how to deal with these issues. Treatments like psychotherapy, behavioral therapy, group therapy, and family therapy. These treatments can be done in a room with a professional in a calm environment, surrounding by people who support you.
“Insulin and I” was a bit disturbing because doctors gave institutional patients insulin against their will. Now, insulin is used for those who suffer from diabetes. Giving insulin to those who do not need it made patients react in many disturbing ways. For example, some would sweat like a clam and go into a hypoglycemic coma (Eghigian, 275). To bring those individuals back to consciousness they would give them sugar (Eghigian, 278). This article was anonymous which leads readers to maybe question its credibility. The patient in this article is all happy about insulin because they think it is helping them, when in reality it is not.
Cognitive therapy or CBT was a successful innovation. It is something still used today by many professionals. Beck was simplistic in his study and how is is not necessary to explore someone’s past to know why they are the way they are (Eghigian, 383). There are some differences between cognitive and behavioral therapy. Cognitive is more about the thinking aspect and how that can be changed. For example, a person being so anxious they can’t stop thinking about something happening to them or their family may seek help for someone to advise them on how to cope with those thoughts and how to prevent them.
Along with therapy treatments, medication was a treatment as well. In the article about Osheroff, Osheroff filed a lawsuit against the Chestnut Lodge for refusing to give him appropriate medication (Eghigian, 405). Osheroff was at Chestnut Hill for a long period of time because they never really treated his illness. He lost a lot of weight, was agitated, and had insomnia (Eghigian, 406). Osheroff also had many failed marriages and it was mentioned that he went to marital therapy but it did not save his marriage (Eghigian, 414). That goes to show everyone is different and not all treatments will work the same on everyone.
The data portion below have very similar therapy techniques percentage wise. The years were close considering it was 2010 and 2019 but there were some slight changed. Thankfully, electroconvulsive therapy has the lowest percent. That seems like the one that would do the most damage. That is definitely not a therapy Pinel would be in favor of.
In conclusion, there are many different forms of therapy and treatment for those in institutions. Examples can be seen below. Reading passages on therapy and treatments in the 1900s there are some similarities to now. Some definitely work better than others. But it depends on the person and their circumstances.
Sources
Eghigian, Greg, ed. 2010. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. New Brunswick, N.J.: Rutgers University Press. INSERT-MISSING-URL.
load("~/Madness/nmhss_2019_puf_R.RData")
load("~/Madness/N-MHSS-2010-DS0001-data-r.rda")rename data sets
treatments_2019 <- PUF
treatments_2010 <- da34945.0001Clean 2019
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
treat_cleaned_2019 <- treatments_2019 %>%
#select treatment variables
select(starts_with("TREAT")) %>%
pivot_longer(cols = everything(), names_to = "treatment", values_to = "count") %>%
#compute percentages of facilities that offer the treatment
group_by(treatment) %>%
summarise(percent2019 = mean(count)) %>%
mutate(treatment = case_when(
treatment == "TREATPSYCHOTHRPY" ~ "psychotherapy",
treatment == "TREATFAMTHRPY" ~ "family therapy",
treatment == "TREATGRPTHRPY" ~ "group therapy",
treatment == "TREATCOGTHRPY" ~ "cognitive behavioral therapy",
treatment == "TREATDIALTHRPY" ~ "dialectical behavior therapy",
treatment == "TREATBEHAVMOD" ~ "behavior modification",
treatment == "TREATDUALMHSA" ~ "integrated dual disorders treatment",
treatment == "TREATTRAUMATHRPY" ~ "trauma therapy",
treatment == "TREATACTVTYTHRPY" ~ "activity therapy",
treatment == "TREATELECTRO" ~ "electroconvulsive therapy",
treatment == "TREATTELEMEDINCE" ~ "telemedicine therapy",
treatment == "TREATPSYCHOMED" ~ "psychotropic medication",
treatment == "TREATOTH" ~ "mentalhealth treatment approach",
treatment == "TREATMT" ~ "substanceuse treatment",
TRUE ~ as.character(treatment)))
treat_cleaned_2019## # A tibble: 14 x 2
## treatment percent2019
## <chr> <dbl>
## 1 activity therapy 0.446
## 2 behavior modification 0.660
## 3 cognitive behavioral therapy 0.902
## 4 dialectical behavior therapy 0.563
## 5 integrated dual disorders treatment 0.566
## 6 electroconvulsive therapy 0.0410
## 7 family therapy 0.725
## 8 group therapy 0.856
## 9 substanceuse treatment 0.563
## 10 mentalhealth treatment approach 0.0585
## 11 psychotropic medication 0.818
## 12 psychotherapy 0.916
## 13 telemedicine therapy 0.380
## 14 trauma therapy 0.773Clean 2010 data
treat_cleaned_2010 <- treatments_2010 %>%
select(starts_with("TREAT")) %>%
pivot_longer(cols = everything(), names_to = "treatment", values_to = "count") %>%
mutate(count = readr::parse_number(as.character(count))) %>%
group_by(treatment) %>%
summarise(percent2010 = mean(count, na.rm = TRUE)) %>%
mutate(treatment = case_when(
treatment == "TREATPSYCHOTHRPY" ~ "psychotherapy",
treatment == "TREATFAMTHRPY" ~ "family therapy",
treatment == "TREATGRPTHRPY" ~ "group therapy",
treatment == "TREATCOGTHRPY" ~ "cognitive behavioral therapy",
treatment == "TREATDIALTHRPY" ~ "dialectical behavior therapy",
treatment == "TREATBEHAVMOD" ~ "behavior modification",
treatment == "TREATDUALMHSA" ~ "integrated dual disorders treatment",
treatment == "TREATTRAUMATHRPY" ~ "trauma therapy",
treatment == "TREATACTVTYTHRPY" ~ "activity therapy",
treatment == "TREATELECTRO" ~ "electroconvulsive therapy",
treatment == "TREATTELEMEDINCE" ~ "telemedicine therapy",
treatment == "TREATPSYCHOMED" ~ "psychotropic medication",
treatment == "TREATOTH" ~ "mentalhealth treatment approach",
treatment == "TREATMT" ~ "substanceuse treatment",
TRUE ~ as.character(treatment)))
treat_cleaned_2010## # A tibble: 11 x 2
## treatment percent2010
## <chr> <dbl>
## 1 activity therapy 0.515
## 2 behavior modification 0.655
## 3 cognitive behavioral therapy 0.887
## 4 integrated dual disorders treatment 0.549
## 5 electroconvulsive therapy 0.0634
## 6 family therapy 0.675
## 7 group therapy 0.852
## 8 mentalhealth treatment approach 0.0835
## 9 psychotropic medication 0.837
## 10 psychotherapy 0.863
## 11 telemedicine therapy 0.157Join data
treat <- full_join(treat_cleaned_2010, treat_cleaned_2019) %>%
mutate(percent2010 = ifelse(is.na(percent2010),0,percent2010),
treatment = forcats::fct_reorder(treatment,percent2019)) %>%
pivot_longer(cols = -treatment, names_to = "year", values_to = "percent") %>%
mutate(year = stringr::str_remove(year, "percent"))## Joining, by = "treatment"treat## # A tibble: 28 x 3
## treatment year percent
## <fct> <chr> <dbl>
## 1 activity therapy 2010 0.515
## 2 activity therapy 2019 0.446
## 3 behavior modification 2010 0.655
## 4 behavior modification 2019 0.660
## 5 cognitive behavioral therapy 2010 0.887
## 6 cognitive behavioral therapy 2019 0.902
## 7 integrated dual disorders treatment 2010 0.549
## 8 integrated dual disorders treatment 2019 0.566
## 9 electroconvulsive therapy 2010 0.0634
## 10 electroconvulsive therapy 2019 0.0410
## # ... with 18 more rowsVisualize data
library(ggplot2)
ggplot(treat,
aes(x = percent,
y = treatment,
fill = year)) +
geom_bar(stat = "identity", position = "dodge") +
scale_x_continuous(labels = scales::percent_format())+
labs(title = "Popular Mental Illness Treatments in the US",
y = NULL,
x = "Percent of Facilities",
caption = "Data Source: SAMHSA, National Mental Health Services Survey")