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Fig. 1. Showing incremental changes in pre- and post- intervention scores on problem behaviour for physically abused adolescents

Figure 1 Showing incremental changes in pre- and post- intervention scores on problem behaviour for physically abused adolescents

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Top 10 Major Topical Areas in JAP Articles as Indexed by PsycINFO (2007-2015)
Top 10 Major Topical Areas in JAP Articles as Indexed by PsycINFO (2007-2015)
Table 1: The modified adjective checklist used for the present study
Table 1: The modified adjective checklist used for the present study
(M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
(M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
Table 3: Frequency and percentage of list of attributes selected by employee respondents across different organizations to describe an effective male and female leaders (M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
Table 3: Frequency and percentage of list of attributes selected by employee respondents across different organizations to describe an effective male and female leaders (M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
Table 4: Five most frequently chosen attributes of effective male and female leaders by male and female employees (M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
Table 4: Five most frequently chosen attributes of effective male and female leaders by male and female employees (M) Masculine Attributes, (F) Feminine Attributes, (N) Neutral Attributes
Table 1. Common aims and objectives of the three associations
Table 1. Common aims and objectives of the three associations
All associations, whether national or local, can elect their representatives to form the General Council of the Federation from which the Executive Council of the Federation is elected. At the same time, each member can get affiliated to the Federation at an extra cost of the membership. Thus, all the members would get the benefits of being member of the Federation - in terms of subscription to journal or registration for conferences. The advantages are that 1. The existing structure of the associations does not get affected 2. All the members will have individual membership with the Federation and 3. Each member will have the responsibility of electing the office bearers of the Federation. The disadvantages are that 1. Itis very cumbersome even to develop the bylaws of this type of Federation, due to dual membership issues. 2. It will be difficult to differentiate who is a
All associations, whether national or local, can elect their representatives to form the General Council of the Federation from which the Executive Council of the Federation is elected. At the same time, each member can get affiliated to the Federation at an extra cost of the membership. Thus, all the members would get the benefits of being member of the Federation - in terms of subscription to journal or registration for conferences. The advantages are that 1. The existing structure of the associations does not get affected 2. All the members will have individual membership with the Federation and 3. Each member will have the responsibility of electing the office bearers of the Federation. The disadvantages are that 1. Itis very cumbersome even to develop the bylaws of this type of Federation, due to dual membership issues. 2. It will be difficult to differentiate who is a
Fig 3: Federation Model Ill On similar lines of the UPA CR, the Federation can be headed by a representative of one of the associations on a rotation basis. For example, during the first term, the President will be from Association-A, the President Elect from Association B, Secretary General from Association C and the Treasurer from Association D. Similarly, the representation to different international psychology associations can also planned in rotation.
Fig 3: Federation Model Ill On similar lines of the UPA CR, the Federation can be headed by a representative of one of the associations on a rotation basis. For example, during the first term, the President will be from Association-A, the President Elect from Association B, Secretary General from Association C and the Treasurer from Association D. Similarly, the representation to different international psychology associations can also planned in rotation.
Fig 4: The six Cs Ahead
Fig 4: The six Cs Ahead
Note: This is the revised and updated paper of the papers presented at: 1. Symposium, 48th National Conference and 17th International conference of IAAP Karnatak University Dharwad, (May 21-23, 2013,), 2. Symposium, 40th National Annual Conference of Indian Association of Clinical Psychologists, (February 28, 2014 ) held at SRM University, Chennai and ‘Observations’ made during the Presidential Symposium on Working Together for a Common Cause at the 21st NAOP conference held at Christ University in December, 11-13, 2012.
Note: This is the revised and updated paper of the papers presented at: 1. Symposium, 48th National Conference and 17th International conference of IAAP Karnatak University Dharwad, (May 21-23, 2013,), 2. Symposium, 40th National Annual Conference of Indian Association of Clinical Psychologists, (February 28, 2014 ) held at SRM University, Chennai and ‘Observations’ made during the Presidential Symposium on Working Together for a Common Cause at the 21st NAOP conference held at Christ University in December, 11-13, 2012.
Table 1. General Characteristic Features of the Employees in the present study
Table 1. General Characteristic Features of the Employees in the present study
Figure 1: A Graphical representation of the levels of Psychological Capital (and its Components) of the Employees The results (Table-2) explicitly reveal that the employees of the pharmaceutical organization have their mean values quite high in terms of Psychological Capital (4.266) and all its dimensions namely, Self-efficacy (4.421), Hope (4.336), Resiliency (4.063) and Optimism (4.245). In other words, the Psychological Capital (along with its components) is predominant among the employees as evident from their score distribution culminating towards the higher scale continuum. Further, the scrutiny of the results revealed that Self-efficacy and Resiliency have the highest and lowest mean values of 4.421 and 4.063, respectively. Hence, the hypothesis, H, which states that ‘The mean scores of PsyCap are towards the higher end of the scale.’ is accepted.
Figure 1: A Graphical representation of the levels of Psychological Capital (and its Components) of the Employees The results (Table-2) explicitly reveal that the employees of the pharmaceutical organization have their mean values quite high in terms of Psychological Capital (4.266) and all its dimensions namely, Self-efficacy (4.421), Hope (4.336), Resiliency (4.063) and Optimism (4.245). In other words, the Psychological Capital (along with its components) is predominant among the employees as evident from their score distribution culminating towards the higher scale continuum. Further, the scrutiny of the results revealed that Self-efficacy and Resiliency have the highest and lowest mean values of 4.421 and 4.063, respectively. Hence, the hypothesis, H, which states that ‘The mean scores of PsyCap are towards the higher end of the scale.’ is accepted.
Table 2. Mean and SD of Psychological Capital & its Components of the Employees Levene’s Test of equality for variances with respect to Psychological Capital shows a significance level of 0.072, which is greater than 0.05 and thus there is a statistical evidence that the variances are equal across the groups and that two-way ANOVA can be conducted.
Table 2. Mean and SD of Psychological Capital & its Components of the Employees Levene’s Test of equality for variances with respect to Psychological Capital shows a significance level of 0.072, which is greater than 0.05 and thus there is a statistical evidence that the variances are equal across the groups and that two-way ANOVA can be conducted.
One-Sample Kolmogorov-Smirnov Test with respect to the two variables, namely, Psychological Capital and Happiness of the employees reveal that both the variables are normally distributed. The Kolmogorov-Smirnov significance values of Psychological Capital (0.184) and Happiness (0.282) are greater than 0.05, indicating that the data is normal and thus ensuring the implementation of parametric tests. Psychological Capital, Demography and Employee Happiness
One-Sample Kolmogorov-Smirnov Test with respect to the two variables, namely, Psychological Capital and Happiness of the employees reveal that both the variables are normally distributed. The Kolmogorov-Smirnov significance values of Psychological Capital (0.184) and Happiness (0.282) are greater than 0.05, indicating that the data is normal and thus ensuring the implementation of parametric tests. Psychological Capital, Demography and Employee Happiness
The main effect of gender is not significant, F(1,96) = 3.42, p = .068, whereas the main effect of job category is highly significant, F(1,96) = 56.03, p < .001. However, the interaction of gender and job category is not significant, F(1,96) = 2.86, p = .094. Hence, the two hypotheses, H, and H, that state that ‘There is a significant main effect for gender on PsyCap of the employees’ and ‘There is a significant gender by job category interaction on PsyCap of the employees,’ respectively are rejected. Based on the results, hypothesis H, which states that ‘There is a significant main effect for job category on PsyCap of the employees’ has been accepted. Figure 2 depicts a crystallizing evidence of the stated results. Table 3. Two-Way ANOVA: Descriptive Statistics of Employees on PsyCap Composite Average Score with respect to gender and job category.
The main effect of gender is not significant, F(1,96) = 3.42, p = .068, whereas the main effect of job category is highly significant, F(1,96) = 56.03, p < .001. However, the interaction of gender and job category is not significant, F(1,96) = 2.86, p = .094. Hence, the two hypotheses, H, and H, that state that ‘There is a significant main effect for gender on PsyCap of the employees’ and ‘There is a significant gender by job category interaction on PsyCap of the employees,’ respectively are rejected. Based on the results, hypothesis H, which states that ‘There is a significant main effect for job category on PsyCap of the employees’ has been accepted. Figure 2 depicts a crystallizing evidence of the stated results. Table 3. Two-Way ANOVA: Descriptive Statistics of Employees on PsyCap Composite Average Score with respect to gender and job category.
Figure 3: AScattergram depicting the linear relationship between Psychological Capital and Happiness of the Employees
Figure 3: AScattergram depicting the linear relationship between Psychological Capital and Happiness of the Employees
Table 4. Simple Linear Regression between Psychological Capital (Predictor) and Happiness of the Employees
Table 4. Simple Linear Regression between Psychological Capital (Predictor) and Happiness of the Employees
Note. 6 = negative affectivity; 7 = positive affectivity; 8 = subjective well-being. **p < .01.
Note. 6 = negative affectivity; 7 = positive affectivity; 8 = subjective well-being. **p < .01.
Table 2. Multiple Regression Analysis for Resilience, Self-efficacy, Hope and Optimism Predicting SWB, Negative Emotions and Positive Emotions (N = 616) and contribute to the flourishing or optimal functioning of people, groups, and institutions. It focuses on positive aspects of human nature, like joy and altruism, and creates healthy families and institutions as addressing the full spectrum of human experience. These positive topics of inquiry are important to understand in their own context, and not only as buffers against the problems, stressors, and disorders of life (Gable & Haidt, 2005).
Table 2. Multiple Regression Analysis for Resilience, Self-efficacy, Hope and Optimism Predicting SWB, Negative Emotions and Positive Emotions (N = 616) and contribute to the flourishing or optimal functioning of people, groups, and institutions. It focuses on positive aspects of human nature, like joy and altruism, and creates healthy families and institutions as addressing the full spectrum of human experience. These positive topics of inquiry are important to understand in their own context, and not only as buffers against the problems, stressors, and disorders of life (Gable & Haidt, 2005).
Asignificant gender difference was seen on dimensions of PE (t=-2.64, df=190, P<0,001) on FMPS and Avoidant Emotion Focused coping on the Brief COPE (t=2.63, df=190, P<0.01) with female participants scoring higher on both. Table 1. Scores on measures of perfectionism, procrastination, coping and psychological outcomes and difference across genders (N=192)
Asignificant gender difference was seen on dimensions of PE (t=-2.64, df=190, P<0,001) on FMPS and Avoidant Emotion Focused coping on the Brief COPE (t=2.63, df=190, P<0.01) with female participants scoring higher on both. Table 1. Scores on measures of perfectionism, procrastination, coping and psychological outcomes and difference across genders (N=192)
Significance (2 tailed) *p<.05, **p<.001 Note: FMPS= Frost Multidimensional Perfectionism Scale; CM= Concern over Mistakes; PE= Parental Expectation; PC=Parental Criticism; DOA= Doubts over action; O=Organization; AP=Adaptive Perfectionism; MP= Maladaptive Perfectionism Table 2.Associations between perfectionism, procrastination and coping (N=192) perfectionism (personal standards and organization) had a significant, negative association with procrastination, suggesting that with greater levels of organization and higher personal standards there is reduced procrastination (Table 2).
Significance (2 tailed) *p<.05, **p<.001 Note: FMPS= Frost Multidimensional Perfectionism Scale; CM= Concern over Mistakes; PE= Parental Expectation; PC=Parental Criticism; DOA= Doubts over action; O=Organization; AP=Adaptive Perfectionism; MP= Maladaptive Perfectionism Table 2.Associations between perfectionism, procrastination and coping (N=192) perfectionism (personal standards and organization) had a significant, negative association with procrastination, suggesting that with greater levels of organization and higher personal standards there is reduced procrastination (Table 2).
Significance (2 tailed) *P<.05, **P<.001 Note: FMPS= Frost Multidimensional Perfectionism Scale; CM= Concern over Mistakes; PE= Parental Expectation; PC=Parental Criticism; DOA= Doubts over action: O=Organization; AP=Adaptive Perfectionism; MP= Maladaptive Perfectionism; DASS=Depression, Anxiety and Stress Scale Table 3. Associations between procrastination perfectionism, and self-esteem and distress (N=192)
Significance (2 tailed) *P<.05, **P<.001 Note: FMPS= Frost Multidimensional Perfectionism Scale; CM= Concern over Mistakes; PE= Parental Expectation; PC=Parental Criticism; DOA= Doubts over action: O=Organization; AP=Adaptive Perfectionism; MP= Maladaptive Perfectionism; DASS=Depression, Anxiety and Stress Scale Table 3. Associations between procrastination perfectionism, and self-esteem and distress (N=192)
Table 1. Showing the composition of abused group
Table 1. Showing the composition of abused group
Table 2. t-ratio of Pre- and Post-Intervention Scores of Abused Adolescents on YSR
Table 2. t-ratio of Pre- and Post-Intervention Scores of Abused Adolescents on YSR
The average incremental reduction for each participant in this category is 38.95 points on the problem behaviour score. On an average, each participant’s post-intervention scores becomes 38.95 points lower than his/her pre-intervention scores on the measure of problem behaviour.
The average incremental reduction for each participant in this category is 38.95 points on the problem behaviour score. On an average, each participant’s post-intervention scores becomes 38.95 points lower than his/her pre-intervention scores on the measure of problem behaviour.
Fig.2. Showing incremental changes in pre- and post- intervention scores on problem behaviour for sexually abused adolescents
Fig.2. Showing incremental changes in pre- and post- intervention scores on problem behaviour for sexually abused adolescents
The average incremental reduction for each participant in this category is 46.10 points on the problem behaviour score. On an average, each participant’s post-intervention scores becomes 46.10 points lower than his/her pre-intervention scores on YSR.
The average incremental reduction for each participant in this category is 46.10 points on the problem behaviour score. On an average, each participant’s post-intervention scores becomes 46.10 points lower than his/her pre-intervention scores on YSR.
Table 3. Showing the average incremental changes on problem behaviour measure for each category of childhood abuse (N=20)
Table 3. Showing the average incremental changes on problem behaviour measure for each category of childhood abuse (N=20)
Table 1: Socio-demographic details of the study and control groups
Table 1: Socio-demographic details of the study and control groups
From Table 1 it is evident that more number of fathers were educated up to the level of graduation and were working compared to mothers.
From Table 1 it is evident that more number of fathers were educated up to the level of graduation and were working compared to mothers.
Table 3: Comparison of study and control groups on ICSRLE, SAF, and CCL during pre and post assessments
Table 3: Comparison of study and control groups on ICSRLE, SAF, and CCL during pre and post assessments
a= the sample size for different tools did not remain the same; * p - Significant at 0.05 level; p - Significant at 0.01 level; NS — p not significant
a= the sample size for different tools did not remain the same; * p - Significant at 0.05 level; p - Significant at 0.01 level; NS — p not significant
Table 1. Matrix of bivariate correlations between Attitude towards Religious Diversity and the Factor: of Multicultural Personality along with Means and Standard Deviations (n = 236) Note: M = Mean, Standard Deviations are written in parentheses** p< 0.01
Table 1. Matrix of bivariate correlations between Attitude towards Religious Diversity and the Factor: of Multicultural Personality along with Means and Standard Deviations (n = 236) Note: M = Mean, Standard Deviations are written in parentheses** p< 0.01
Table 2. Standardized Beta Coefficients of Predictors of Attitude towards Religious Diversity (n = 236)
Table 2. Standardized Beta Coefficients of Predictors of Attitude towards Religious Diversity (n = 236)
Table 2 showing the model coefficients of the mediation analysis
Table 2 showing the model coefficients of the mediation analysis
Note: The reliability coefficient appears in the parentheses along with the main diagonal. *p<.05 “p<.01 ***p<.001. Table 1 showing correlations and descriptive analysis of the study variables.
Note: The reliability coefficient appears in the parentheses along with the main diagonal. *p<.05 “p<.01 ***p<.001. Table 1 showing correlations and descriptive analysis of the study variables.
Table-1. Reliability Assessment of SRD-1 (N = 222; Scale CA = 0.323) Validity Assessment of SRD-1
Table-1. Reliability Assessment of SRD-1 (N = 222; Scale CA = 0.323) Validity Assessment of SRD-1
Table-4.Validity Assessment of SRD-3 (N = 222) A single factor was obtained with Eigen value of 2.497 explaining variance of 62.415%. Unidimensional nature of SRD and construct validity of SRD was thus proved. Validity assessment of SRD-3 also did not offer any cue for further improvement.
Table-4.Validity Assessment of SRD-3 (N = 222) A single factor was obtained with Eigen value of 2.497 explaining variance of 62.415%. Unidimensional nature of SRD and construct validity of SRD was thus proved. Validity assessment of SRD-3 also did not offer any cue for further improvement.
Table-3. Reliability Assessment of SRD-3 (N = 222; Scale CA = 0.797)
Table-3. Reliability Assessment of SRD-3 (N = 222; Scale CA = 0.797)
Table 1. Percentages of the participants who commented on different aspects of the measure The results of above Table ensured a good face validity of the measure. However, participants were also asked to report verbally regarding difficulty of items, typos, time
Table 1. Percentages of the participants who commented on different aspects of the measure The results of above Table ensured a good face validity of the measure. However, participants were also asked to report verbally regarding difficulty of items, typos, time
Note. n = 500, average inter-item correlation = 35, average item-total correlation = .63, ** 9 < .01 (2-tailed). * p < .05 (2-tailed). Table 2. Correlation matrix (R-matrix 2) for SES
Note. n = 500, average inter-item correlation = 35, average item-total correlation = .63, ** 9 < .01 (2-tailed). * p < .05 (2-tailed). Table 2. Correlation matrix (R-matrix 2) for SES
data. The KMO value is .92. Bartlett's test of sphericity indicated a y2 value of 3100.77 (p < .001). All this together supports the factorability of the R-matrix. Data for the 16-item SES were therefore subjected to EFA. Method of Principal Component (PC) with varimax rotation was used. The initial analysis with Eigen value > 1.00 (the Kaiser-Guttman criterion) extracted 3-factor solution, accounting for 55.48% of the total variance (Data not shown). However, an inspection of the scree plot indicates a clear break after the 3rd component (Figure 1) leading us to retain three components (Karim & Nigar, 2014)
data. The KMO value is .92. Bartlett's test of sphericity indicated a y2 value of 3100.77 (p < .001). All this together supports the factorability of the R-matrix. Data for the 16-item SES were therefore subjected to EFA. Method of Principal Component (PC) with varimax rotation was used. The initial analysis with Eigen value > 1.00 (the Kaiser-Guttman criterion) extracted 3-factor solution, accounting for 55.48% of the total variance (Data not shown). However, an inspection of the scree plot indicates a clear break after the 3rd component (Figure 1) leading us to retain three components (Karim & Nigar, 2014)
These are ses10 (.82) for the latent variable ‘Emotional Engagement’, ses15 (.74) for ‘Cognitive Engagement’, and ses3 (.65) and ses4 (.65) for ‘Emotional Engagement’. The lowest factor loading was for ses7 (.31) under the latent variable ‘Behavioural Engagement’. Correlations among the latent variables varied from .65 to .69. Table 3. Factor loadings from exploratory and confirmatory factor analysis of SES (Three-factor model) The convergent validity of the Bangla version SES was examined by estimating inter-factor correlations and the factor-total correlations. The inter-factor correlations (Pearson’s r) were all positively significant with the coefficient of .03 to .57. The three factors also significantly correlated with the entire SES, the coefficient ranging from .74 to .89.
These are ses10 (.82) for the latent variable ‘Emotional Engagement’, ses15 (.74) for ‘Cognitive Engagement’, and ses3 (.65) and ses4 (.65) for ‘Emotional Engagement’. The lowest factor loading was for ses7 (.31) under the latent variable ‘Behavioural Engagement’. Correlations among the latent variables varied from .65 to .69. Table 3. Factor loadings from exploratory and confirmatory factor analysis of SES (Three-factor model) The convergent validity of the Bangla version SES was examined by estimating inter-factor correlations and the factor-total correlations. The inter-factor correlations (Pearson’s r) were all positively significant with the coefficient of .03 to .57. The three factors also significantly correlated with the entire SES, the coefficient ranging from .74 to .89.
The experimental task involved disengaging indicating the new neutral word by pressing one of two keys. Disengagement of attention was operationalized as time taken to inhibit the primed response and indicate the neutral target. The paradigm is graphically represented below:
The experimental task involved disengaging indicating the new neutral word by pressing one of two keys. Disengagement of attention was operationalized as time taken to inhibit the primed response and indicate the neutral target. The paradigm is graphically represented below:
Table 1. Mean reaction time and standard error in msecs for the two levels of each of the two independent variables
Table 1. Mean reaction time and standard error in msecs for the two levels of each of the two independent variables
Table 2. Mean reaction time and standard deviation for each of the four interactions of visual field and valence Atwo way repeated measure of ANOVA was carried out on the data. Results are reported in Table 3. No main effect was found for either of the variables i.e. for visual field (F(1,29)= 0.88, p= 0.36) or valence (F(1,29)=0.46, p= 0.5). However, a significant interaction effect (F(1,29)= 4.43, p= 0.04, partial eta-squared= 0.13) was found as shown graphically in Figure 2 & Table 3.
Table 2. Mean reaction time and standard deviation for each of the four interactions of visual field and valence Atwo way repeated measure of ANOVA was carried out on the data. Results are reported in Table 3. No main effect was found for either of the variables i.e. for visual field (F(1,29)= 0.88, p= 0.36) or valence (F(1,29)=0.46, p= 0.5). However, a significant interaction effect (F(1,29)= 4.43, p= 0.04, partial eta-squared= 0.13) was found as shown graphically in Figure 2 & Table 3.
Mean reaction time for stimuli presented to the left visual field, that is, the right hemisphere was 946.76 msec as compared to 936.13 msec for stimuli presented to the right visual field, o1 left hemisphere. With respect to the second independent variable, valence, the mean reaction time for positive stimuli was 938.34 msec as compared to 944.56 msec for the negative stimuli. All descriptive statistics are reported in Table 2.
Mean reaction time for stimuli presented to the left visual field, that is, the right hemisphere was 946.76 msec as compared to 936.13 msec for stimuli presented to the right visual field, o1 left hemisphere. With respect to the second independent variable, valence, the mean reaction time for positive stimuli was 938.34 msec as compared to 944.56 msec for the negative stimuli. All descriptive statistics are reported in Table 2.
Note. *Means and standard deviations of predictor variables. Table 1. Pearson’s product-moment correlation coefficients between predictor variables and criterion variables
Note. *Means and standard deviations of predictor variables. Table 1. Pearson’s product-moment correlation coefficients between predictor variables and criterion variables
Table 2. Hierarchical multiple linear regression model of adherence Note. B = Unstandardised Beta Coefficient, SEB = Standard Error of Beta, B = Standardised Beta Coefficient, t = t-test, p = probability
Table 2. Hierarchical multiple linear regression model of adherence Note. B = Unstandardised Beta Coefficient, SEB = Standard Error of Beta, B = Standardised Beta Coefficient, t = t-test, p = probability
Note. B = Unstandardised Beta Coefficient, SEB = Standard Error of Beta, 8 = Standardisex Beta Coefficient, t = t-test, p = probability. Table 3. Hierarchical multiple linear regression model of prognosis
Note. B = Unstandardised Beta Coefficient, SEB = Standard Error of Beta, 8 = Standardisex Beta Coefficient, t = t-test, p = probability. Table 3. Hierarchical multiple linear regression model of prognosis
Table-1. Mean and S.D. of Sample Characteristics
Table-1. Mean and S.D. of Sample Characteristics
Table-2. Mean, S.D. and Correlation Coefficients of Measured Variables Table-1 reveals that the mean age of the spouses, which was 36.33 (S.D.=7.11) years The mean of years of education of was 5.9( (S.D.=5.24) years. The mean duration o marriage was 17.26 (S.D.=7.27) years and the spouses were exposed to the Spousal UTES fo a a ee eee a ae Ps) Py Expressed Emotion (EE) is linked to high rates of relapse in psychiatric disorders. There can be an array of factors like burden of care, family problems, marital discord, financial problems, personality attributes, severity and chronicity of the disorder, which can contribute to high rates of EE. In the present study, two variables (a) Burden of care and (b) Social support were chosen to explore their relationship
Table-2. Mean, S.D. and Correlation Coefficients of Measured Variables Table-1 reveals that the mean age of the spouses, which was 36.33 (S.D.=7.11) years The mean of years of education of was 5.9( (S.D.=5.24) years. The mean duration o marriage was 17.26 (S.D.=7.27) years and the spouses were exposed to the Spousal UTES fo a a ee eee a ae Ps) Py Expressed Emotion (EE) is linked to high rates of relapse in psychiatric disorders. There can be an array of factors like burden of care, family problems, marital discord, financial problems, personality attributes, severity and chronicity of the disorder, which can contribute to high rates of EE. In the present study, two variables (a) Burden of care and (b) Social support were chosen to explore their relationship
i” i. Se Please enroll my name as Life/Annual/Student Member of the Indian Academy of Applied
i” i. Se Please enroll my name as Life/Annual/Student Member of the Indian Academy of Applied
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Memory (Cognitive Psychology)Neurodevelopmental Disorders
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