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Does going to university in a different country affect your mental health? A Japanese international university surveyed its students in 2018 and published a study the following year that was approved by several ethical and regulatory boards.

The study found that international students have a higher risk of mental health difficulties than the general population, and that social connectedness (belonging to a social group) and acculturative stress (stress associated with joining a new culture) are predictive of depression.

Explore the students data using PostgreSQL to find out if you would come to a similar conclusion for international students and see if the length of stay is a contributing factor.

Here is a data description of the columns you may find helpful.

Field NameDescription
inter_domTypes of students (international or domestic)
japanese_cateJapanese language proficiency
english_cateEnglish language proficiency
academicCurrent academic level (undergraduate or graduate)
ageCurrent age of student
stayCurrent length of stay in years
todepTotal score of depression (PHQ-9 test)
toscTotal score of social connectedness (SCS test)
toasTotal score of acculturative stress (ASISS test)
Hidden code students
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DataFrameas
df
variable
-- Original exercise here
SELECT 
	stay AS stay, 
	COUNT(inter_dom) AS count_int, 
	ROUND(AVG(todep), 2) AS average_phq, 
	ROUND(AVG(tosc), 2) AS average_scs, 
	ROUND(AVG(toas), 2) AS average_as
FROM students
WHERE students.inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay DESC
LIMIT 9;

Lets determine if the calculated average metrics can help us prevent suicide in international students:

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DataFrameas
suicide_metrics
variable
-- Finding out if a set of metrics can help prevent suicide in international students
SELECT stay AS stay, COUNT(inter_dom) AS count_int, ROUND(AVG(todep), 2) AS average_phq, ROUND(AVG(tosc), 2) AS average_scs, ROUND(AVG(toas), 2) AS average_as, suicide AS life_ended
FROM students
WHERE students.inter_dom = 'Inter' AND toas < 92
GROUP BY stay, suicide 
ORDER BY stay DESC;

No conclusion so far, the current state of the metrics cannot predict whether a person will end their life or not. However if we obtain Minimum and Maximum values we find the following:

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DataFrameas
gender_suicide
variable
-- Adding more metrics: 
SELECT suicide AS life_ended,
	COUNT(gender) AS gender_count,
	gender,
	ROUND(MAX(todep), 2) AS max_phq,
	ROUND(MIN(todep), 2) AS min_phq,
	ROUND(MAX(tosc), 2) AS max_scs,
	ROUND(MIN(tosc), 2) AS min_scs,
	ROUND(MAX(toas), 2) AS max_as,  
	ROUND(MIN(toas), 2) AS min_as
FROM students
WHERE students.inter_dom = 'Inter'
GROUP BY gender, suicide
ORDER BY life_ended DESC;

Can we conclude that those individuals with a phq score of 6, shall receive psycological treatment to avoid suicide? Lets explore some more metrics:

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DataFrameas
df1
variable