Unit 2 entailed all things involving institutions and their history. Common terms for those institutions are asylums, madhouses, and mental hospitals. There was definitely a stigma surrounding asylums and madhouses because of the stories that surfaced. Today, popular culture has also visualized said stigma with movies and tv shows. Some of these institutions were public and some were private. The public institutions were often loud, confined, busy, and urban while private institutions were more rural and for folk with higher class. Private institutions housed elite clientele and those members were often seen as embarrassing towards their family. While the growth of institutions occurred there was also deinstitutionalization that occurred.

Deinstitutionalization entails how the community care of asylums, mental hospitals, and “madhouses” lost a large portion of their patients. A lot of different factors led to the drop in numbers which is noticeable from the graphs below. Especially with the White race. These graphs specifically look at the drop from 1940 to 1970 in the United States. The drop in numbers also led to these individuals homeless, in prison, in nursing homes, etc. Once people left on their own free will or were discharged some stories were not very pleasant about the time in the institution.

The Limerick Lunatic Asylum looked into the admitted and their illnesses. They produced annual reports of their “lunatics”. Some were marked as incurable while others were curable. It was interesting to see the different forms of illnesses, some including mania, idiocracy, dementia, imbecility, melancholia, and many more (Eghigian, 148). The majority of those in the Limerick Asylum were from the laboring class (Eghigian, 150). Which raises the question of how their families were able to support themselves when they were institutionalized.

Writer Elizabeth Packard who in her time was a champion for married women’s rights wrote about the stories some females were faced with and the medical abuse they endured while institutionalized (Eghigian, 162). One example is Mrs. Cheneworth who was a woman of good class and education. She endured medical abuse such as being subdued, choked, kicked, drowned, strangled, and more (Eghigian, 165). The workers in the institution refused many of her requests and her life sadly ended by her own hands as she could not take the abuse anymore. She was brought to the asylum against her will by her husband. There is no question of the hierarchy of gender.

Emil Kraepelin had a strong influence on modern psychiatry as it was becoming understood based on its symptoms (Eghigian, 200). Sleeping meds were introduced in this chapter as doctors started to treat with medication. Surveillance in wards was being conducted as a way to monitor the patients. The administration of the sleeping pills were for both genders however a lot of the medicine for males were because of the hope to ease the disruption and injury (Eghigian, 205). Sulfate and opium were the drugs used most often on these patients (Eghigian, 206). Today, giving opium and other hard narcotics to patients seems extreme and something that would be given in absolutely necessary.

It was clear that the mentally ill needed better resources and treatment. There were many things wrong with how the system was running and if the patients were actually cared for and in the best hands possible. This is where deinstitutionalization comes in. Many institutions started losing their patients for numerous reasons. It can be seen below the drop in population. Interestingly, the black race had an increase in institutionalization. Then became prevention and how society can prevent those from having to go into the institution. Much talk of genetics and Darwinsim became mentioned. That is because people are born a certain way and some people are more predisposed to illness than others. Early recognition and assessment can be beneficial as the psychology profession and education became more common. There became more resources for individuals who were seeking help but family stress was high. Difficulty arouse as individuals were leaving these facilities and no place to go. As institutions became being publicly funded tax dollars had to go to them and some more conservative individuals were not in favor of their money being out towards that cause.

In conclusion, it can be seen that institutionalization changed tremendously over the years. Thankfully, there are more resources now and the stigma is less as harsh. Thanks to more individuals studying psychology and more knowledge about how everyone is affected with mental health. Deinstitutionalization has both pros and cons. As people are starting to leave the institutions some have nowhere to go. However, if they do have a place to go, like the institution tax payers may not be in favor of where their money is going. Furthermore, for the better there are more options and treatments such as medicine, therapy, early assessment, and counseling that can help with those suffering from issues with mental health.

MLA Works Cited

Eghigian, Greg, editor. From Madness to Mental Health : Psychiatric Disorder and Its Treatment in Western Civilization. Rutgers University Press, 2010. INSERT-MISSING-DATABASE-NAME, INSERT-MISSING-URL. Accessed 27 Mar. 2021.


## Decennial Census, 1940





```r
library(readr)
## Warning: package 'readr' was built under R version 4.0.3
pop_1940 <- read.csv("~/Madness/institutionalPop_1940.csv")


# pint data 
pop_1940
##   Variable Pop_Total Pop_Mental
## 1    Total 101102924     591355
## 2     Male  50553748     317812
## 3   Female  50549176     273553
## 4    White  91428165     536629
## 5     Male  45823031     288238
## 6   Female  45605134     248391
## 7 Nonwhite   9674759      54736
## 8     Male   4730717      29574
## 9   Female   4944042      25162

Clean data

# delete rows: 1,2,3,4,7
pop_1940_cleaned <- pop_1940[-c(1,2,3,4,7),]

# print data
pop_1940_cleaned
##   Variable Pop_Total Pop_Mental
## 5     Male  45823031     288238
## 6   Female  45605134     248391
## 8     Male   4730717      29574
## 9   Female   4944042      25162
# rename values
pop_1940_cleaned[1,1] <- "White_Male"
pop_1940_cleaned[2,1] <- "White_Female"
pop_1940_cleaned[3,1] <- "Nonwhite_Male"
pop_1940_cleaned[4,1] <- "Nonwhite_Female"

# print data 
pop_1940_cleaned
##          Variable Pop_Total Pop_Mental
## 5      White_Male  45823031     288238
## 6    White_Female  45605134     248391
## 8   Nonwhite_Male   4730717      29574
## 9 Nonwhite_Female   4944042      25162
# load package
library(dplyr) #for %>%
## Warning: package 'dplyr' was built under R version 4.0.4
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr) #for separate () function
## Warning: package 'tidyr' was built under R version 4.0.4
# separate Variable to two columns: Race and Gender
pop_1940_processed <-
pop_1940_cleaned %>%
  separate(col = Variable, into = c("Race", "Gender"), sep = "_")

# print data
pop_1940_processed
##       Race Gender Pop_Total Pop_Mental
## 5    White   Male  45823031     288238
## 6    White Female  45605134     248391
## 8 Nonwhite   Male   4730717      29574
## 9 Nonwhite Female   4944042      25162

Decennial Census, 1970

Import Data

pop70_cleaned <- read.csv("~/Madness/pop70_cleaned.csv")

# print data 
pop_1940_cleaned
##          Variable Pop_Total Pop_Mental
## 5      White_Male  45823031     288238
## 6    White_Female  45605134     248391
## 8   Nonwhite_Male   4730717      29574
## 9 Nonwhite_Female   4944042      25162

join data

pop_40N70 <- pop_1940_processed %>%
select(-Pop_Total) %>%
  left_join(pop70_cleaned, by = c("Race", "Gender")) %>%
  rename (Pop_Mental_1940 = Pop_Mental,
         Pop_Mental_1970 = Pop_Mental_70)

pop_40N70
##       Race Gender Pop_Mental_1940 Pop_Mental_1970
## 1    White   Male          288238          194405
## 2    White Female          248391          157578
## 3 Nonwhite   Male           29574           45567
## 4 Nonwhite Female           25162           29227
pop_long <- pop_40N70 %>% 
  pivot_longer(3:4, names_to = "Census_Year", values_to = "Pop_Mental")%>%
  mutate(Year = stringr::str_remove(Census_Year, "Pop_Mental_")) %>%
  select(-Census_Year)

pop_long
## # A tibble: 8 x 4
##   Race     Gender Pop_Mental Year 
##   <chr>    <chr>       <int> <chr>
## 1 White    Male       288238 1940 
## 2 White    Male       194405 1970 
## 3 White    Female     248391 1940 
## 4 White    Female     157578 1970 
## 5 Nonwhite Male        29574 1940 
## 6 Nonwhite Male        45567 1970 
## 7 Nonwhite Female      25162 1940 
## 8 Nonwhite Female      29227 1970
library(scales)
## Warning: package 'scales' was built under R version 4.0.3
## 
## Attaching package: 'scales'
## The following object is masked from 'package:readr':
## 
##     col_factor
# Interested in the change between 40 and 70, overall 
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.3
pop_long %>%
  group_by(Year) %>%
  summarise(Pop = sum(Pop_Mental)) %>%
  ggplot(aes(x = Year, y = Pop)) +
  geom_col() + 
  scale_y_continuous(labels = comma_format()) + 
  labs(y = "Institutional Population")

 title = "Overall Change in Institutionalization"
# Interested in the change between 40 and 70, race 
pop_long %>%
  group_by(Year, Race) %>%
  summarise(Pop = sum(Pop_Mental)) %>%
  ggplot(aes(x = Year, y = Pop, fill = Race)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = comma_format())
## `summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.

# Interested in the change between 40 and 70, overall 
pop_long %>%
  group_by(Year, Gender) %>%
  summarise(Pop = sum(Pop_Mental)) %>%
  ggplot(aes(x = Gender, y = Pop, fill = Year)) +
  geom_col(position = "dodge") + 
  scale_y_continuous(labels = comma_format())
## `summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.