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Time Period and Birth Cohort Differences in Depressive Symptoms in the U.S., 1982–2013

Authors:
Jean M. Twenge at San Diego State University
Jean M. Twenge
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Abstract and Figures

Across four surveys (N = 6.9 million), Americans reported substantially higher levels of depressive symptoms, particularly somatic symptoms, in the 2000s–2010s compared to the 1980s–1990s. High school students in the 2010s (vs. the 1980s) reported more somatic symptoms (e.g., trouble sleeping, thinking, and remembering; shortness of breath) and were twice as likely to have seen a professional for mental issues. College students in recent years (vs. the 1980s) were more likely to report feeling overwhelmed and to believe they were below average in mental and physical health, but were less likely to say they felt depressed. Total Center for Epidemiological Studies Depression scores were higher among adults in 2000 (vs. 1988), especially somatic symptoms. Teens displayed less suicidal ideation in 2011 versus 1991 and were slightly less likely to commit suicide. Thus, more subtle symptoms of depression became more prevalent even as some overt indicators of depression became less prevalent.
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All content in this area was uploaded by Jean M. Twenge on Aug 18, 2015
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&(&EH*-"(+%1E'2$315%!"#$%&'>-?#0$%+,?'%)*'>-?#0$%+,$#'DE$*/<$"&"@?1'F37%I>RgIKL5%
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4+"2$'1"-3%G'$115'
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J.*+B$1%"+%/(E+1$H"+B%/$+-$'%/H"$+-%&'(,H$#1%*/'(11%8>%3$*'15%>,"9/--$")%&'>-?#0"&"@?B'
./-/%,#0'%)*'>,%#+$#/1'AF7%LL<R;5%
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&(&EH*-"(+F%C"1$+-*+BH"+B%*B$%*+)%/(.('-%$00$/-15%!"#$%&'>-?#0$%+,?'%)*'>-?#0$%+,$#'
DE$*/<$"&"@?1'FH7%9=><98I5%
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$#(-"(+*H%&'(,H$#1%"+%U+BH*+)F%D%/(#&*'"1(+%(0%-A(%+*-"(+*H%/(.('-1%-A$+-3%3$*'1%*&*'-5%
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&'$2*H$+/$%(0%#*N('%)$&'$11"(+%*+)%/(#(',")%1E,1-*+/$%E1$%)"1(')$'1%"+%-.$%4+"-$)%6-*-$1%
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%
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%)*'5/*$#$)/1'II7%8:98<89=K5%%
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I7%;:K<;9=5%
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&'$2*H$+/$%(0%#*N('%)$&'$11"(+%"+%*+%DE1-'*H"*+%/(##E+"-3%1*#&H$%,$-A$$+%899:%*+)%
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d(()A"+7%_5%C5%P;==>Q5%!.$%&'$2*H$+/$%(0%&*+"/%*--*/W1%"+%-.$%4+"-$)%6-*-$1F%89:=%-(%
899I5%7"8,)%&'"9'=&$)$#%&'DE$*/<$"&"@?1'IJ7%98K<98L5%
dE$('BE"$2*7%_57%@*HH"+/W'()-7%J57%c%\'31-*H7%?5%[5%P;=88Q5%!'*N$/-('"$1%(0%C$&'$11"(+%
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;:5%
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"+/'$*1"+B%&'$2*H$+/$5%7"8,)%&'"9':99/#+$L/'O$-",*/,-1'2FP7%;=I<;8K5%
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%
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8K=5%
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2P+0T@,%*/'-8,L/?U7%8998<;=8=%uJ(#&E-$'%0"H$1%*+)%/()$,((Wv5%fJG6_;I>:;<2;5%D++%D',('7%
@fF%f+-$'<E+"2$'1"-3%J(+1('-"E#%0('%G(H"-"/*H%*+)%6(/"*H%_$1$*'/.5%
\*11$'7%!57%c%_3*+7%_5%@5%P899LQ5%YE'-.$'%$a*#"+"+B%-.$%D#$'"/*+%)'$*#F%C"00$'$+-"*H%
/(''$H*-$1%(0%"+-'"+1"/%*+)%$a-'"+1"/%B(*H15%>/,-")%&$+?'%)*'!"#$%&'>-?#0"&"@?'V8&&/+$)1'337%
;:=<;:R5%
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?(.+1(+7%e5!5%P;==:Q5%f+"-"*H%1$2$'"-3%*+)%*+-")$&'$11*+-%,$+$0"-1F%*%#$-*<*+*H31"1%(0%)*-*%
1E,#"--$)%-(%-.$%Y(()%*+)%C'EB%D)#"+"1-'*-"(+5%'>W"!'5/*7%I7%$KI5%
\H$'#*+7%d5%b57%c%^$"11#*+7%@5%@5%P89:9Q5%f+/'$*1"+B%'*-$1%(0%)$&'$11"(+5%7"8,)%&'"9'
+0/':</,$#%)'5/*$#%&':--"#$%+$")1'3J27%;;;9<;;>I5%
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>-?#0"&"@?1'2P37%88=<8;=5%
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-.*-%/(+-'",E-$%-(%"#&('-*+-%&*-.A*31%*11(/"*-$)%A"-.%#*N('%)$&'$11"(+F%C"$-7%1H$$&7%*+)%
$a$'/"1$5%7"8,)%&'"9':99/#+$L/'O$-",*/,-1'2FN7%8;<;R5%
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@*/W$+l"$7%J5%657%U'"/W1(+7%?57%C$*+$7%Y57%c%^'"B.-7%@5%P;=8KQ5%J.*+B$1%"+%*--"-E)$1%
-(A*')%1$$W"+B%#$+-*H%.$*H-.%1$'2"/$1F%D%K=<3$*'%/'(11<-$#&('*H%#$-*<*+*H31"15%=&$)$#%&'
>-?#0"&"@?'./L$/61'AF7%99<8=L5%
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/(+1-"-E-"(+5%>/,-E/#+$L/-'")'>-?#0"&"@$#%&'!#$/)#/1'I7%K;=<K>=5%
@*-.$'17%J5C57%c%b(+/*'7%C5%P;==LQ5%G'(N$/-"(+1%(0%BH(,*H%#('-*H"-3%*+)%,E')$+%(0%
)"1$*1$%0'(#%;==;%-(%;=>=5%>W"!'5/*'A7%;=88g;=>=5%
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*)(H$1/$+-%'$1&(+1$%&*--$'+1%(+%-.$%@@GfV@@Gf<D%*/'(11%0(E'%)$/*)$15%7"8,)%&'"9'
>/,-")%&$+?':--/--</)+1'N27%RK<:K5%
TH01(+7%@57%c%@*'/E17%65%J5%P;==9Q5%S*-"(+*H%&*--$'+1%"+%*+-")$&'$11*+-%#$)"/*-"(+%
-'$*-#$+-5%:,#0$L/-'"9'M/)/,%&'>-?#0$%+,?1'JJ7%:K:<:IL5%
G*--$+7%65%e5%P;==>Q5%_$/*HH%,"*1%*+)%#*N('%)$&'$11"(+%H"0$-"#$%&'$2*H$+/$5%!"#$%&'
>-?#0$%+,?'%)*'DE$*/<$"&"@?1'AN7%;9=<;9L5%
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(2$'F%4+"-$)%6-*-$17%;==I<;==:5%4%+$")%&'=/)+/,'9",'C/%&+0'!+%+$-+$#-'O%+%'V,$/9'HJ7%T/-(,$'%
;=885%
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Q,/-0<%)B'Q",+?TR/%,'X,/)*-1'2YJJT3PPJ5%b(1%D+B$H$1F%["B.$'%U)E/*-"(+%_$1$*'/.%f+1-"-E-$5%
_$3+(H)17%?57%6-$A*'-7%@57%@*/C(+*H)7%_57%c%6"1/.(7%b5%P;==LQ5%[*2$%*)(H$1/$+-1%
,$/(#$%-((%*#,"-"(E1i%["B.%1/.((H%1$+"('1`%$)E/*-"(+*H%*+)%(//E&*-"(+*H%&H*+17%89RL%-(%
;===5%!"#$%&'>,";&/<-1'IA7%8:L<;=L5%
%
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c%_$B"$'7%C5%D5%P89:KQ5%b"0$-"#$%&'$2*H$+/$%(0%1&$/"0"/%&13/."*-'"/%)"1(')$'1%"+%-.'$$%1"-$15%
:,#0$L/-'"9'M/)/,%&'>-?#0$%+,?1'F27%9K9<9I:5%
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P;=88Q5%6(#*-"/V*00$/-"2$%13#&-(#17%,E-%+(-%/(B+"-"2$V*00$/-"2$%13#&-(#17%(0%)$&'$11"(+%
*0-$'%*/E-$%/*')"*/%13+)'(#$%*'$%*11(/"*-$)%A"-.%8;<#(+-.%*HH</*E1$%#('-*H"-35%7"8,)%&'"9'
:99/#+$L/'O$-",*/,-1'2A27%8I:<8L>5%
6/.*"$7%\5%^5%P89LIQ5%D%B$+$'*H%#()$H%0('%-.$%1-E)3%(0%)$2$H(&#$+-*H%&'(,H$#15%
>-?#0"&"@$#%&'V8&&/+$)7%JF7%9;<8=R5%'
6$H"B#*+7%@5%U5%G5%P89::Q5%e((#$'%,HE$15%>-?#0"&"@?'X"*%?1'331'I=<I>5%
6.*0$'7%D5%e5%P;==LQ5%@$-*<*+*H31"1%(0%-.$%0*/-('%1-'E/-E'$1%(0%0(E'%)$&'$11"(+%
nE$1-"(++*"'$1F%e$/W7%JU6<C7%[*#"H-(+7%*+)%wE+B5%7"8,)%&'"9'=&$)$#%&'>-?#0"&"@?1'J37%8;><
8KL5%
!A$+B$7%?5%@5%P;===Q5%!.$%*B$%(0%*+a"$-3i%e"'-.%/(.('-%/.*+B$%"+%*+a"$-3%*+)%
+$E'(-"/"1#7%89I;<899>5%7"8,)%&'"9'>/,-")%&$+?'%)*'!"#$%&'>-?#0"&"@?1'HY1%8==R<8=;85%%%
!A$+B$7%?5%@5%P;==LQ5%M/)/,%+$")'5/B'Z0?'X"*%?[-'R"8)@':</,$#%)-':,/'5",/'
=")9$*/)+1':--/,+$L/1'D)+$+&/*TT%)*'5",/'5$-/,%;&/'X0%)'DL/,'V/9",/G%S$A%]('WF%Y'$$%G'$115%%
!A$+B$7%?5%@57%J*#&,$HH7%^5%\57%c%d$+-"H$7%e5%P;=8;Q5%d$+$'*-"(+*H%"+/'$*1$1%"+%*B$+-"/%
1$H0<$2*HE*-"(+1%*#(+B%D#$'"/*+%/(HH$B$%1-E)$+-17%89LL<;==95%!/&9'%)*'(*/)+$+?1'227%K=9<
K;R5%%
!A$+B$7%?5%@57%d$+-"H$7%e57%C$^*HH7%J5%S57%@*7%C5%657%b*/$0"$H)7%\57%c%6/.E'-l7%C5%_5%P;=8=Q5%
e"'-.%/(.('-%"+/'$*1$1%"+%&13/.(&*-.(H(B3%*#(+B%3(E+B%D#$'"/*+17%89>:<;==RF%D%/'(11<
-$#&('*H%#$-*<*+*H31"1%(0%-.$%@@Gf5%=&$)$#%&'>-?#0"&"@?'./L$/61'AP7%8KI<8IK5%%
%
>=%
!A$+B$7%?5%@57%c%f#7%J5%P;==RQ5%J.*+B$1%"+%-.$%+$$)%0('%1(/"*H%*&&'(2*H7%89I:<;==85%
7"8,)%&'"9'./-/%,#0'$)'>/,-")%&$+?1'F27%8R8<8:95%%
!A$+B$7%?5%@57%c%S(H$+<[($W1$#*7%65%P;==;Q5%DB$7%B$+)$'7%'*/$7%1(/"($/(+(#"/%
1-*-E17%*+)%,"'-.%/(.('-%)"00$'$+/$1%(+%-.$%J."H)'$+`1%C$&'$11"(+%f+2$+-('3F%D%#$-*<
*+*H31"15%7"8,)%&'"9':;)",<%&'>-?#0"&"@?1'2221%IR:<I::5%%
^"/W'*#*'*-+$7%G5%?57%^$"11#*+7%@5%@57%b$*07%G5%?57%c%[(H0(')7%!5%_5%P89:9Q5%DB$7%&$'"()7%
*+)%/(.('-%$00$/-1%(+%-.$%'"1W%(0%#*N('%)$&'$11"(+F%_$1EH-1%0'(#%0"2$%4+"-$)%6-*-$1%
/(##E+"-"$15%7"8,)%&'"9'=&$)$#%&'DE$*/<$"&"@?1'F37%>>><>K>5%
%
%
>8%
Table 1: U.S. 12th graders’ physical and mental symptoms and treatment seeking, 1982-2012
1982-
84
1985-
89
1990-
94
1995-
99
2000-
04
2005-
09
2010-
12
d (82-
84 vs.
10-
12)
d (82-
84 vs.
05-
09)
Mental issues (index)
2.25
(1.14)
2.34
(1.18)
2.44
(1.25)
2.48
(1.32)
2.50
(1.36)
2.50
(1.38)
2.44
(1.39)
.15
.20
Trouble remembering things
1.65
(1.34)
1.75
(1.44)
1.87
(1.56)
1.99
(1.68)
2.12
(1.76)
2.10
(1.75)
2.05
(1.74)
.26
.29
Difficulty thinking or
concentrating
2.16
(1.59)
2.23
(1.64)
2.36
(1.72)
2.40
(1.77)
2.43
(1.81)
2.48
(1.84)
2.47
(1.88)
.18
.18
Trouble learning new things
1.63
(1.24)
1.68
(1.29)
1.75
(1.36)
1.73
(1.37)
1.74
(1.39)
1.76
(1.40)
1.75
(1.38)
.09
.10
Trouble sleeping
2.48
(1.67)
2.56
(1.76)
2.60
(1.81)
2.70
(1.91)
2.72
(1.92)
2.74
(1.95)
2.79
(2.03)
.17
.14
Trouble getting started in the
morning
3.35
(2.16)
3.52
(2.22)
3.62
(2.26)
3.60
(2.30)
3.51
(2.31)
3.40
(2.25)
3.20
(2.25)
-.07
.02
Breathing issues (index)
1.30
(.83)
1.36
(.92)
1.46
(1.06)
1.50
(1.11)
1.43
(1.02)
1.44
(1.01)
1.46
(1.04)
.17
.15
Shortness of breath when you
were not exercising
1.35
(1.04)
1.43
(1.13)
1.53
(1.27)
1.58
(1.33)
1.51
(1.22)
1.52
(1.24)
1.56
(1.27)
.18
.15
Wheezing or gasping
1.25
(.89)
1.30
(.98)
1.40
(1.12)
1.43
(1.15)
1.36
(1.06)
1.37
(1.05)
1.36
(1.05)
.10
.09
Physical ailments (index)
2.36
(1.03)
2.41
(1.06)
2.51
(1.10)
2.54
(1.12)
2.43
(1.09)
2.37
(1.09)
2.32
(1.08)
-.03
-.01
Headache
3.07
(1.57)
3.19
(1.60)
3.30
(1.64)
3.31
(1.64)
3.25
(1.68)
3.12
(1.69)
3.12
(1.69)
.03
.03
Sore throat or hoarse voice
2.53
(1.55)
2.55
(1.55)
2.59
(1.53)
2.59
(1.55)
2.52
(1.53)
2.43
(1.51)
2.36
(1.49)
-.11
-.06
Sinus congestion, runny nose,
sneezing
3.33
(1.86)
3.42
(1.88)
3.51
(1.85)
3.58
(1.86)
3.40
(1.89)
3.31
(1.86)
3.27
(1.88)
-.03
-.01
Coughing spells
2.11
(1.58)
2.14
(1.59)
2.26
(1.61)
2.31
(1.64)
2.20
(1.60)
2.14
(1.56)
2.07
(1.55)
-.03
.02
Chest cold
1.64
(1.26)
1.67
(1.29)
1.78
(1.36)
1.76
(1.34)
1.67
(1.26)
1.62
(1.22)
1.55
(1.17)
-.07
-.02
%
>;%
Coughing up phlegm or blood
1.47
(1.23)
1.52
(1.28)
1.67
(1.41)
1.72
(1.46)
1.60
(1.34)
1.58
(1.33)
1.56
(1.31)
.07
.08
Days stayed home because you
were not feeling well
1.76
(1.16)
1.77
(1.15)
1.77
(1.20)
1.78
(1.20)
1.77
(1.19)
1.79
(1.21)
1.76
(1.23)
.00
.03
Rating of relative physical
health
3.61
(1.00)
3.59
(1.03)
3.45
(1.06)
3.40
(1.04)
3.34
(1.07)
3.34
(1.07)
3.31
(1.10)
-.29
-.26
Visits to doctor or other
professional
Routine physical check-up
1.72
(.87)
1.71
(.85)
1.77
(.93)
1.81
(.94)
1.88
(.96)
1.89
(.95)
1.93
(.97)
.23
.19
% in the last 12 months
52%
52%
53%
56%
59%
60%
62%
Injury from fight, assault, or auto
accident
1.14
(.56)
1.16
(.63)
1.18
(.70)
1.16
(.65)
1.18
(.67)
1.15
(.61)
1.14
(.59)
.00
.02
% in the last 12 months
8.2%
8.8%
9.2%
8.7%
9.4%
8.3%
7.9%
Other accidental injury
1.34
(.84)
1.34
(.85)
1.34
(.87)
1.33
(.83)
1.36
(.92)
1.32
(.84)
1.34
(.89)
.01
-.02
% in the last 12 months
19%
19%
19%
17%
19%
17%
17%
Physical illness or symptom
1.74
(1.13)
1.75
(1.14)
1.79
(1.19)
1.84
(1.19)
1.83
(1.17)
1.79
(1.15)
1.79
(1.16)
.04
.04
% in the last 12 months
39%
40%
41%
43%
43%
41%
41%
Emotional or psychological
problem or symptom
1.11
(.59)
1.14
(.67)
1.14
(.67)
1.18
(.77)
1.18
(.74)
1.22
(.83)
1.23
(.85)
.17
.15
% in the last 12 months
4.3%
5.7%
5.7%
7.0%
8.0%
9.1%
9.3%
NOTE: All d’s .04 and above are significantly different at p < .05 or below.
%
>>%
Table 2: Entering college students’ self-reports of mental health issues, 1985-2013
1985-
89
1990-
94
1995-
99
2000-
04
2005-
09
2010-
13
d (85-
89 vs.
10-
13)
d (85-
89 vs.
05-
09)
20%
24%
29%
27%
28%
30%
.23
.17
9%
9%
9%
8%
7%
7%
-.09
-.09
39%
41%
43%
46%
45%
48%
.19
.13
37%
41%
42%
44%
44%
44%
.14
.14
NOTE: All d’s .02 and above are significantly different at p < .05 or below.
%
>K%
Table 3: Changes in depressive symptoms on the CES-D among adults in the Longitudinal Study of Generations,
1988-2000%
1988
2000
d
t
All 19 and older
Negative affect
9.60 (3.31)
10.01 (3.53)
.12
3.31***
Somatic symptoms
10.60 (3.28)
11.47 (3.51)
.26
7.06***
Positive affect
13.23 (2.77)
13.41 (2.56)
.07
1.85
Relations with others
2.40 (.84)
2.57 (.99)
.18
5.20***
CES-D total score
9.21 (8.07)
10.53 (8.69)
.16
4.25***
Felt depressed (Bradburn single
item)
31%
31%
.00
.00
Ages 30-39
Negative affect
9.99 (3.38)
10.36 (3.83)
.11
.94
Somatic symptoms
10.86 (3.43)
11.94 (4.19)
.30
2.53*
Positive affect
13.34 (2.50)
12.80 (2.70)
-.21
-1.94*
Relations with others
2.54 (.93)
2.70 (1.08)
.17
1.45
CES-D total score
9.84 (8.17)
12.11 (10.20)
.27
2.18*
Felt depressed (Bradburn single
item)
40%
33%
-.14
1.42
NOTES: 1. d = difference in terms of standard deviation
2. * = p < .05, ** = p < .01, *** = p < .001
%
>I%
Table 4: Suicidal ideation, suicide attempts, and suicide rates among U.S. 9th-12th graders, 1991-2011
1991-
94
1995-
99
2000-
04
2005-
09
2010-
11
d (91-
94 vs.
10-11)
d (91-
94 vs.
05-09)
Felt sad or hopeless for two or
more weeks
------
28%
29%
28%
29%
.00
-.02
Considered suicide
26%
21%
18%
15%
16%
-.25
-.29
Made a plan to commit suicide
19%
16%
16%
12%
13%
-.16
-.21
Attempted to commit suicide
8%
8%
9%
7%
8%
-.01
-.03
Medically treated after attempt
2%
3%
3%
2%
2%
.01
-.02
Actual suicide rate, 15-24 year
olds
.0134%
.0114%
.0100%
.0090%
.0105%
-.02
-.03
NOTE: All d’s .04 and above are significantly different at p < .05 or below. For the actual suicide rate, significance
testing was not performed as the rates include the entire population and not a sample.
%
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;==K%
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... These findings were further validated by a study conducted by Twenge [15], who found the reason for these age and gender differences between the different generations and showed significant generational differences in American birth cohorts [15]. The socalled generation of "Millennials" (born in the period ranging from 1982 to 1999) was more likely to express symptoms of depression than the generation of "Gen Xers" (born in the period ranging from 1961 to 1981). ...
... These findings were further validated by a study conducted by Twenge [15], who found the reason for these age and gender differences between the different generations and showed significant generational differences in American birth cohorts [15]. The socalled generation of "Millennials" (born in the period ranging from 1982 to 1999) was more likely to express symptoms of depression than the generation of "Gen Xers" (born in the period ranging from 1961 to 1981). ...
... The socalled generation of "Millennials" (born in the period ranging from 1982 to 1999) was more likely to express symptoms of depression than the generation of "Gen Xers" (born in the period ranging from 1961 to 1981). A possible reason for this consists in the constant changes in culture, as society has transformed considerably over the last few decades, with fundamental changes in relationships, technology, and medicine; this cultural shift has resulted in generational differences in personality traits, attitudes, and behaviors [15]. Another reason consists in the rising prevalence of digitalization and the use of social media, which may increase the prevalence of depression and anxiety [16]. ...
Differences between Germans in the ‘Young’, ‘Adult’, and ‘Over-40s’ Age Groups Regarding Symptoms of Depression and Anxiety and Satisfaction with Life
Article
Full-text available
  • Jan 2024
Depression and anxiety, the most prevalent mental disorders worldwide, are among the top four mental disorders in Germany, and both impact life satisfaction. The prevalence of depression, anxiety, and life satisfaction in different age groups has not been sufficiently examined. The present cross-sectional study of a non-clinical sample of a German-speaking population analyzes the links between age—specifically, certain life stages—as predictors for depression and anxiety symptoms and life satisfaction. Therefore, three age groups were formed from all the participants (N = 478): ‘Young’ (18–24 years), ‘Adult’ (25–39 years), and ‘Over-40s’ (40 years and older). The German versions of the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Scale-7 (GAD-7), and the German Quality of Life Questionnaire (FLZ-A) were used for our analysis. Our statistical analysis consisted of χ2 tests and an ANCOVA for determining the associations between categorical variables. The Over 40s age group showed statistically significantly higher life satisfaction than the ‘Adult’ age group. Comparing levels of depressive or anxiety symptoms, there were no statistically significant differences across the age groups. These findings highlight the significance of considering age as a factor in understanding mental health and well-being. Further research is warranted to investigate supplementary factors that could potentially contribute to the variations observed within the different age groups.
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... , 以致部分人失 去真实自我与奋斗意义, 甚至选择"躺平" (张自慧 等, 2022)。当然, 这可能并非中国特有的现象, 困 境(Cai et al., 2012;Deci & Ryan, 2000;Kasser, 2003;Twenge, 2000;Twenge, 2015; ...
More utilitarian and less rational? Social change and two types of individualism over the last 40 years in China
Article
Full-text available
  • May 2024
  • Acta Psychol Sin
Individualism appears to have increased along with modernization and globalization, yet it is a great debate whether such a cultural shift fell in the value discrepancy between the independence-focused rational individualism and the interest-focused utilitarian individualism, especially in fast-changing societies like China. Based on expert interview, self-report survey, and open-question analysis (pilot study), the pilot study established a reliable and valid dictionary of rational individualism and utilitarian individualism, finding that rational (vs. utilitarian) individualism prevailed in responses to questions about rational individualism, and vice versa. Furthermore, based on word counting (Study 1) and word embedding (Study 2) analyses of Chinese version of Google Books Ngram (1980~2019), the present research was designed to test the effect of social change on rational individualism and utilitarian individualism. We hypothesized that (1) rational individualism decreased while utilitarian individualism increased from 1980 through 2019, and that (2) the semantic association between self and rational (vs. utilitarian) individualism decreased over the past 40 years. As expected, Study 1 revealed that the usage of rational individualism decreased, while that of utilitarian individualism increased over time; and via the single-target Word Embedding Association Test (WEAT), Study 2 revealed that the semantic similarity between the target words about self (e.g., I, self) and attribute words about rational (vs. utilitarian) individualism decreased over time. Taken together, the results demonstrate the cultural shift of the increase in utilitarian individualism and decrease in rational individualism over the past 40 years in China, whereas both rational enlightenment and utilitarian expansion serve as psychological drives in the development of modern societies. It was suggested that the value discrepancy of rational and utilitarian individualism should be seriously concerned, and that further work is needed on multiple selves, cultural evolution, and psychological function of the two types of individualism.
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... Therefore, whereas these services make surveying easier and faster, the samples are likely less representative of a world reality, and while oversampling women may help promote women's health, other groups also warrant consideration. Building upon our work, future studies should also investigate underrepresented gender groups and sexual and racial/ethnic diversities (Brance et al., 2023) to help promote equity (Weersing et al., 2022), as well as other demographic variables of interest such as age generation differences (Twenge, 2011;Twenge, 2015). Finally, the ISS has general limitations that are described in the project's OSF page (https://osf. ...
Cross-cultural validation and measurement invariance of anxiety and depression symptoms: A study of the Brief Symptom Inventory (BSI) in 42 countries
Article
  • Apr 2024
  • J AFFECT DISORDERS
Background. Depression and anxiety are among the most prevalent mental health issues experienced worldwide. However, whereas cross-cultural studies utilize psychometrically valid and reliable scales, fewer can meaningfully compare these conditions across different groups. To address this gap, the current study aimed to psychometrically assess the Brief Symptomatology Index (BSI) in 42 countries. Methods. Using data from the International Sex Survey (N = 82,243; Mage = 32.39; SDage = 12.52; women: n = 46,874; 57 %), we examined the reliability of depression and anxiety symptom scores of the BSI-18, as well as evaluated evidence of construct, invariance, and criterion-related validity in predicting clinically relevant variables across countries, languages, genders, and sexual orientations. Results. Results corroborated an invariant, two-factor structure across all groups tested, exhibiting excellent reliability estimates for both subscales. The ‘caseness’ criterion effectively discriminated among those at low and high risk of depression and anxiety, yielding differential effects on the clinical criteria examined. Limitations. The predictive validation was not made against a clinical diagnosis, and the full BSI-18 scale was not examined (excluding the somatization sub-dimension), limiting the validation scope of the BSI-18. Finally, the study was conducted online, mainly by advertisements through social media, ultimately skewing our sample towards women, younger, and highly educated populations. Conclusions. The results support that the BSI-12 is a valid and reliable assessment tool for assessing depression and anxiety symptoms across countries, languages, genders, and sexual orientations. Further, its caseness criterion can discriminate well between participants at high and low risk of depression and anxiety.
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... These pandemic-related increases should also be considered within the context of well-established increases in prevalence of depression symptoms and disorder over the last century [41][42][43][44] with indication this increase is greatest in adolescence. In representative national surveys in the US the prevalence of past-year major depressive episode (MDE) increased by 7.7% from 2009 to 2019 [45]. ...
COVID-19 pandemic impact on adolescent mental health: a reassessment accounting for development
Article
Full-text available
  • Jan 2024
  • EUR CHILD ADOLES PSY
Current prospective reports suggest a pandemic-related increase in adolescent mental health problems. We examine whether age-related change over 11–14 years accounts for this increase. Mothers and adolescents in a UK-based birth cohort (Wirral Child Health and Development Study; WCHADS; N = 737) reported on adolescent depression and behavioural problems pre-pandemic (December 2019–March 2020), mid-pandemic (June 2020–March 2021) and late pandemic (July 2021–March 2022). Analysis used repeated measures models for over-dispersed Poisson counts with an adolescent-specific intercept with age as a time-varying covariate. Maturational curves for girls, but not for boys, showed a significant increase in self-reported depression symptoms over ages 11–14 years. Behavioural problems decreased for both. After adjusting for age-related change, girls’ depression increased by only 13% at mid-pandemic and returned to near pre-pandemic level at late pandemic (mid versus late – 12%), whereas boys’ depression increased by 31% and remained elevated (mid versus late 1%). Age-adjusted behavioural problems increased for both (girls 40%, boys 41%) and worsened from mid- to late pandemic (girls 33%, boys 18%). Initial reports of a pandemic-related increase in depression in young adolescent girls could be explained by a natural maturational rise. In contrast, maturational decreases in boys’ depression and both boys’ and girls’ behavioural problems may mask an effect of the pandemic.
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... Mental health can be defined as a "state of mind characterized by emotional well-being, good behavioural adjustment, relative freedom from anxiety and disabling symptoms, and a capacity to establish constructive relationships and cope with the ordinary demands and stresses of life" (Association, 2023). Mental health can change throughout the life course, creating patterns that vary within and between individuals and across populations, times, and contexts (Prior, Jones & Manley, 2020;Twenge, 2015). Long-term patterns of individual changes in mental health generally include a good pattern, a poor pattern, and changing patterns, although variations by study exist (Bromberger et al., 2019;Engel et al., 2020;Lincoln & Takeuchi, 2010;Kuchibhatla et al., 2012;Holden, Ware & Lee, 2016;Paksarian et al., 2016). ...
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... teenagers around the world have mental disorders (MD) [2]. Over the past few years, this number has grown even more [3], [4]. MD can be challenging not only for the individual who suffers from it but also for his family, friends, and the community as a whole. ...
Machine Learning for Mental Health: A Systematic Study of Seven Approaches for Detecting Mental Disorders
Conference Paper
Full-text available
  • Jun 2023
Mental disorders are a prevalent issue among teenagers. The widespread use of smartphones and social media has revolutionized the way individuals communicate and exchange information with millions of people using these technologies every day. As a result, vast amounts of data are generated, which can be harnessed to improve mental health detection. The increasing prevalence of mental health issues and the demand for quality healthcare services have led to research exploring the potential of machine learning (ML) to address these challenges. This paper provides a systematic study of seven ML approaches used in previous studies to detect mental disorders. The study examines the datasets employed, the accuracy achieved, and the limitations of each ML approach. The seven ML approaches studied in this paper are Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO), Long Short-Term Memory (LSTM), Random Forest (RF), Logistic Regression (LR), Artificial Neural Networks (ANN), and eXtreme Gradient Boosting (XGBoost). These approaches have been utilized in various studies to detect mental disorders and this paper aims to provide a comprehensive understanding of their effectiveness. The findings indicate that machine learning approaches have demonstrated significant potential for the detection of mental disorders, with promising implications for enhancing healthcare services. Additionally, the paper discusses the open research challenges and future directions for mental health.
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... As noted in the first section of this introduction, there is a body of research suggesting the health of young people within Western contexts is improving Twenge (2015) shows that American young people were more likely to report depressive symptoms, especially psychosomatic symptoms, between 2000 and 2010 (vs. 1980 and 1990) suggesting that this trend may be increasing. ...
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Resilience, Mental Well-Being And Quality Of Life Among Students.
Article
  • Apr 2024
This study aims to investigate the relationship between resilience, mental well-being, and quality of life among students. The study hypothesizes that resilience will be positively associated with mental well-being and quality of life among students, and it will mediate the relationship between stress and mental well-being among students. The study also explores gender and age differences in resilience, mental well-being, and quality of life among students. The Brief Resilience Scale, the Warwick-Edinburgh Mental Well-being Scale, and the WHO Quality of Life Scale-brief will be used to measure resilience, mental well-being, and quality of life, respectively.
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An integrative literature review of birth cohort and time period trends in adolescent depression in the United States
Article
Full-text available
  • Jul 2023
  • SOC PSYCH PSYCH EPID
Purpose The aim of this literature review is to examine evidence of time trends and birth cohort effects in depressive disorders and symptoms among US adolescents in peer-reviewed articles from January 2004 to April 2022. Methods We conducted an integrative systematic literature review. Three reviewers participated at different stages of article review. Of the 2234 articles identified in three databases (Pubmed, ProQuest Central, Ebscohost), 10 met inclusion criteria (i.e., adolescent aged United States populations, included information about birth cohort and survey year, focused on depressive symptoms/disorders). Results All 10 articles observed increases in depressive symptoms and disorders in adolescents across recent survey years with increases observed between 1991 and 2020. Of the 3 articles that assessed birth cohort trends, birth cohort trends were less prominent than time period trends. Proposed explanations for increases included social media, economic-related reasons, changes in mental health screening and diagnosis, declining mental health stigma, increased treatment, and, in more recent years, the COVID-19 pandemic. Conclusions Multiple cross-sectional surveys and cohort studies documented rising prevalence of depressive symptoms and disorder among adolescents from 1991 to 2020. Mechanisms driving this increase are still unknown. Research to identify these mechanisms is needed to inform depression screening and intervention efforts for adolescents.
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Age, Gender, Race, Socioeconomic Status, and Birth Cohort Differences on the Children's Depression Inventory: A Meta-Analysis
Article
Full-text available
  • Nov 2002
A within-scale meta-analysis was performed on 310 samples of children (ages 8–16; N = 61,424) responding to the Children's Depression Inventory (CDI). Girls' depression scores stayed steady from ages 8 to 11 and then increased between ages 12 and 16. Boys' CDI scores were stable from ages 8 to 16 except for a high CDI score at age 12. Girls' scores were slightly lower than boys' during childhood, but girls scored higher beginning at age 13. There were no socioeconomic status effects and no differences between White and Black samples. However, Hispanic samples scored significantly higher on the CDI. Analyses for birth cohort showed a slight decrease in boys' CDI scores over time and no change for girls. Longitudinal studies demonstrated a marked testing effect.
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Responses to Nervous Breakdowns in America Over a 40-Year Period
Article
Full-text available
  • Jul 2000
The 1957 and 1976 Americans View Their Mental Health surveys from the Institute of Social Research were partially replicated in the 1996 General Social Survey (GSS) to examine the policy implications of people’s responses to feeling an impending nervous breakdown. Questions about problems in modern living were added to the GSS to provide a profile of the public’s view of mental health problems. Results were compared for 1957, 1976, and 1996. In 1957, 19% of respondents had experienced an impending nervous breakdown; in 1996, 26% had had this experience. Between 1957 and 1996, participants increased their use of informal social supports, decreased their use of physicians, and increased their use of nonmedical mental health professionals. These findings support policies that strengthen informal support seeking and access to effective psychosocial treatments rather than current mental health reimbursement practices, which emphasize the role of primary care physicians.
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Antidepressant use in persons aged 12 and over: United States
Article
  • Jan 2005
Antidepressants are one of the three most commonly used therapeutic drug classes in the United States (1). While the majority of antidepressants are taken to treat depression, antidepressants can also be taken to treat other conditions, like anxiety disorders. This Data Brief provides the most recent estimates of antidepressant use in the U.S. noninstitutionalized population, including prevalence of use by age, sex, race and Hispanic origin, and length of use. This report also describes trends in the prevalence of antidepressant use from 1999–2002 to 2011–2014.
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How Families Still Matter: A Longitudinal Study of Youth in Two Generations
Book
  • Jan 2002
1. Families, generations, and achievement orientations of youth 2. Models and methods of intergenerational influences 3. The changing contexts of family life since the 1960s 4. Educational and occupational aspirations of youth across generations 5. Self-esteem: self-confidence and self-deprecation of youth 6. Value orientations: youth's individualism and materialism across generations 7. Continuity and change in family influences across generations 8. Why families still matter.
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Lifetime Prevalence of Specific Psychiatric Disorders in Three Sites
Article
  • Oct 1984
  • ARCH GEN PSYCHIAT
• Lifetime rates are presented for 15 DSM-III psychiatric diagnoses evaluated in three large household samples on the basis of lay interviewers' use of the Diagnostic Interview Schedule. The most common diagnoses were alcohol abuse and dependence, phobia, major depressive episode, and drug abuse and dependence. Disorders that most clearly predominated in men were antisocial personality and alcohol abuse and dependence. Disorders that most clearly predominated in women were depressive episodes and phobias. The age group with highest rates for most disorders was found to be young adults (aged 25 to 44 years). Correlates with race, education, and urbanization are presented.
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Changes in attitudes toward seeking mental health services: A 40-year cross-temporal meta-analysis
Article
  • Dec 2013
Although rates of treatment seeking for mental health problems are increasing, this increase is driven primarily by antidepressant medication use, and a majority of individuals with mental health problems remain untreated. Helpseeking attitudes are thought to be a key barrier to mental health service use, although little is known about whether such attitudes have changed over time. Research on this topic is mixed with respect to whether helpseeking attitudes have become more or less positive. The aim of the current study was to help clarify this issue using a cross-temporal meta-analysis of scores on Fischer and Turner's (1970) helpseeking attitude measure among university students (N=6796) from 1968 to 2008. Results indicated that attitudes have become increasingly negative over time, r(44)=-0.53, p<0.01, with even stronger negative results when the data are weighted (w) for sample size and study variance, r(44)=-0.63, p<.001. This disconcerting finding may reflect the greater emphasis of Fischer and Turner's scale toward helpseeking for psychotherapy. Such attitudes may be increasingly negative as a result of the unintended negative effects of efforts in recent decades to reduce stigma and market biological therapies by medicalizing mental health problems.
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