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Your Sadness, My Sorrow: Negative Emotion Concordance Within Married Couples in China 1. Introduction

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Your Sadness, My Sorrow: Negative Emotion Concordance within
Married Couples in China

Wanru Xiong
Office of Population Research
Princeton University

Abstract: This paper finds significant evidence for negative emotion concordance within married couples age 45 and above in China using CHARLS data. To understand the mechanism of such concordance and test the hypothesis of emotional contagion effect, I applied instrument variable method, structure equation model and fixed effect model in this dyadic relation context. The results confirm the effect of

spouse’s negative emotion on the respondent’s and support the emotional contagion

effect hypothesis. In general, wives tend to be more sensitive to the emotional condition of their husband. Keywords: dyadic relation; negative emotion; concordance; emotional contagion effect

1.Introduction

“I will laugh with you and cry with you ... in sickness and in health, till death do us apart.” Couples mean it when they take their wedding vows. There is evidence of health concordance within married couples, which means couples’ health conditions, both physical and mental, tend to converge after marriage (Meyler, Stimpson and Peek, 2007). This phenomenon of health concordance within couples is drawing increasing research interests. Published findings of physical health concordance within couples include concordance in blood pressure (Beresford, 1976; Speers et al., 1986; Peek and Markides, 2003; Stimpson and Peek, 2005), chronic conditions (Cheraskin et al., 1968; Bookwala and Schulz, 1996; Ubeda, 1998; Stimpson and Peek, 2005) and obesity (Inoue et al., 1996; Knuiman et al., 1996).
Among the growing number of papers on health concordance within couples, studies on depressive symptoms and distress are the most prevalent (Meyler, Stimpson and Peek, 2007). According to the comprehensive literature review by Meyler, Stimpson, and Peek (2007), there is overwhelming evidence for concordance of depressive symptoms and distress within couples (Tower and Kasl, 1995; Siegel et al., 2004; Kouros and Cummings, 2010). Nevertheless, there are still debates about the underlying mechanisms, among which emotional contagion is a candidate hypothesis. By far, none hypothesis has won the consensus among researchers (Meyler, Stimpson and Peek, 2007). Other researchers focusing on ethnicity and cultural factors in depression concordance reveal ethnic and cultural difference (Peek et al., 2006). However, more than half of these findings are based on small-scale clinical samples, therefore may be subject to sample selection bias and sample size limitations. Besides, as far as I know, there is no published research on this topic in China.
This paper aims to answer the following questions: Are there depression concordance within middle-aged or elderly married couples in China? Is such concordance the result of emotional contagion? If it is, is there a gender difference in the contagion effect? In this paper, I use “China Health and Retirement Longitudinal Study (CHARLS)” data to study depression contagion within married couples aged 45

1and above. I first review and summarize sociological and psychological literature about the empirical findings and the potential theoretical mechanisms of depression concordance within couples, with emphasis on emotional contagion hypothesis. Then I document the depression concordance phenomena using both cross-sectional and panel data from CHARLS. To further investigate these phenomena, I use instrumental variable (IV) methods, structural equation model (SEM) and fixed effect model to identify the emotional contagion effect in this dyadic relation context. I find that depression concordance within married couples aged above 45 in China is significant. The models support the hypothesis of emotional contagion and show gender differences in such contagion effect.
The rest of the paper is organized as follows. Section 2 reviews the empirical findings and theoretical hypotheses from related literature. Section 3 gives details on sample data and variables. Section 4 shows evidence for depression concordance within spouse. Section 5 sets up a contagion model and tests the emotional contagion hypothesis. Empirical findings are presented along with their interpretations. Section 6 finishes with discussions and concluding remarks.

2.Depression concordance within couples

2.1. Depression concordance
Psychologist and sociologist have reported evidence of mental health concordance within couples. Among 58 published papers on mental health concordance articles reviewed by Meyler et al. (2007), only three studies did not find concordance. Evidence of depression concordance is particularly strong. Though concordance involves both parties, wives’ mental health tends to be more “relationship related” than that of their husbands (Meyler, Stimpson, and Peek, 2007; Moffitt, Spence and Goldney, 1986; Peek et al., 2006). The explanation for depression concordance is still unclear, though emotional contagion hypothesis is more frequently suggested.
Among mental health concordance literature, most studies rely on cross-sectional clinical data, and half of them have sample size less than 100 (Meyler, Stimpson and Peek, 2007). Before regression analysis is applied, earlier researchers use variance analysis and t-test. The Recent development of structural equation model is gaining attention from concordance researchers (Meyler, Stimpson and Peek, 2007). Some researchers regard it as a necessary tool to deal with the simultaneous process of mutual convergence (Peek et al., 2006).

2.2. Possible mechanisms and hypotheses
There are four major theories explaining health concordance within couples. All of them fit into the context of depression concordance. According to the comprehensive review by Meyler et al. (2007), few studies have explicitly tested these theories, mainly due to insufficient data.

2.2.1. Assortative mating
Assortative mating is the term used to describe “any systematic departure from random mating” (Vandenberg, 1972. pp.2) Usually, individuals are more likely to marry someone who is similar to themselves, such as in age (Rele, 1965; Lutz, 1918), ethnic group (Kennedy, 1944), education (Lewis and Oppenheimer, 2000) and socioeconomic status (Kalmijn, 1994).

2
There are two potential ways in which assortative mating would lead to depression concordance within couples. First, if assortative mating happens concerning health and mental health in particular, then individuals who are vulnerable to depression are likely to marry a similar spouse. As a result, in survey data, we might observe depression concordance. The other case in which assortative mating might play a role happens when depression is related to factors such as ethnicity and education. To illustrate, assume that Ph.D. students are more vulnerable to depression while they tend to marry another Ph.D., then we would observe highly concordant depression within Ph.D. couples.
Empirically it is hard to identify the above direct and indirect effect of assortative mating. Several studies using longitudinal data supported the assortative mating hypothesis (Eagles et al., 1987; Siegle et al., 2004). The idea in this paper is that if there is evidence that depression concordance varies with the increase of marital duration, then we could treat assortative mating as fixed effect and test other hypotheses in the process of depression concordance.

2.2.2. Shared resource hypothesis
While assortative mating hypothesis emphasizes on the pre-marriage condition, shared resource hypothesis focuses on the shared environment and experiences after marriage. After their wedding, married couples might go through ups and downs, through rich and poor, instead of living happily ever after. All these experiences would translate into shared health risk and fuses for negative emotion (Rose, Mirowsky, and Goldsteen, 1990; Smith and Zick, 1994; Waite, 1995). One difficulty in testing this hypothesis is that the relations between married couples are so close that it 's hard to tell whether an event is private or shared.

2.2.3. Caregiving
Caregiving might not be pleasant. According to Schulz et al. (2001), there is clear evidence that providing informal care can lead to negative emotion and detrimental physical health effects on elderly spousal caregivers. Even stronger effect is found among intense caregivers (Majerovitz, 1995; Sansoni et al. 2004) and female spousal caregivers (Harwood et al., 1998; Thompson et al., 2004) Given there is connection between one spouse’s health condition and the other’s mood, adding that one’s happiness is positively related to physical health condition, we would probably notice depression concordance within couples via the mechanism of caregiving effect.

2.2.4. Emotion contagion
Emotions are contagious (Darwin, 1872; Hatfield, Cacioppo and Rapson, 1994;
Joiner, 1994; Katz, Beach and Joiner, 1999; Barsade, 2002). The metaphor “you are my sunshine” well catches the essence of happiness eradiation. Psychologists refer to the phenomenon that one individual's mood converges to the other's after exposure to his/her mood state as emotional contagion (Gonzales, Castillo, and Swinkels, 2002; Hatfield, Cacioppo and Rapson, 1994). Hatfield et al. (1994) pointed out that emotional contagion is multilevel: “The precipitating stimuli arise from one individual, act upon one or more other individuals, and yield corresponding or complementary emotions.” (pp.5) The emotional and behavioral synchrony as the result of emotional contagion has adaptive utility for the social relationship (Hatfield, Cacioppo and Rapson, 1994). We can see that emotional contagion is intrinsically a social network issue. Totterdell et al. (1998) and Hsee et al. (1990) noticed that emotional contagion varies by the type of social relationship between the individuals. The experimental

3and statistical evidence is consistent with our common sense that people in an enduring and intimate relationship are more susceptible to contagious emotion from each other (Totterdell, Kellett, Teuchmann, and Briner, 1998; Segrin, 2004; Kouros and Cummings, 2010). This contagion effect is particularly true for married couples (Segrin, 2004).
Negative emotions such as depression and distress are nearly, if not the same, as contagious as positive emotions (Joiner and Katz, 1999). Therefore, the concordance of depression and distress, considered as long-term consequences of frequent negative emotional experiences, may partly attribute to emotional contagion. It is likely that during daily talks, couples communicate not only words but also their emotions.
Theoretically, to test the effect of emotion contagion researchers have to control for many confounding variables and deal with two-way causality problem caused by mutual influence. One challenge is to find some stimulus that applies directly only to one spouse but not the other.

3.Data

3.1. Sample
This paper uses data from the China Health and Retirement Longitudinal Study
(CHARLS). It is a survey of the elderly in China, based on a sample of household with a member aged 45 or above. CHARLS adopts multi-stage stratified Probability Proportionate to Size (PPS) sampling. The pilot survey of CHARLS was conducted in Gansu and Zhejiang province in 2008 and collected data from 48 communities/village in 16 counties/districts, covering 2,685 individuals living in 1,570 households. The response rate is 85%. The second wave of the pilot survey followed in 2012. In 2011 baseline national wave of CHARLS, they surveyed about 10,000 households and 17,500 individuals in 150 counties/districts and 450 villages/resident committees. In the survey, comprehensive and detailed information about family respondent (aged 45 or older) and his/her spouse are collected (CHARLS, 2014). The household oriented sampling method and large sample size of CHARLS provides an excellent opportunity to explore health concordance within couples.

3.2. Data description
The sample in this paper includes respondents who were married with their spouse present. In the sample, I excluded respondents who were married but not living with spouse temporarily for a reason such as work excluded from the sample, mainly due to missing information about their spouse. I also exclude few observations with any missing value in critical variables such as marital status and self-rated health. Data are structured in dyad-level. All the data processing work and regression analysis are done with Stata 12.

3.2.1. Cross-sectional data
In models using cross-sectional data, I treat CHARLS national baseline survey
(2011) and pilot wave 2 (2012) as two separate cross-sectional datasets. Each dataset adopts the same criteria of data cleaning. The 2011 and 2012 sample contains 784 and 5904 couples with complete information respectively. Table 1a and Table 2a show the sample statistics (See Appendix Table 1a and Table 2a).

3.2.2. Panel data

4
CHARLS pilot wave 1 (2008) and wave 2 (2012) constitute a short panel. The panel sample includes respondents who were married with the same spouse present in both waves. Demographic and housing characteristics information is based on the survey in 2012. Unfortunately, there are a lot of missing values in the depression score. As a result, the sample size reduced to 478 in the panel after matching, which leads to unclear sample selection bias. Detailed panel sample statistics are shown in Table 3a (See Appendix Table 3a).

3.3. Measure of depression
CHARLS ask about depression symptoms in the health status and functioning section. The depression symptom questionnaire containing ten items is a short-form of the Center for Epidemiologic Studies Depression scale (CES-D10), a standard measure of depression symptom containing 20 items (Radloff, 1977). Components include depressed mood, feelings of hopelessness, fearfulness, loneliness, and sadness, poor concentration and sleep disturbance. Two items are worded in a positive direction to control for response bias. Respondents are asked to rate each item on a scale from 1 to 4 on the basis of “How often you have felt and behaved during the last week”, where 1=rarely or none of the time (<1 day), 2= some or a little of the time (1- 2 days), 3=occasionally or a moderate amount of the time (3-4 days), 4= most or all of the time (5-7 days). The total score is calculated by first reversing scores from item DC013 and DC016 (worded in positive direction), and then summing up all the scores. I replace the missing responses with the mean of the other items. Considering the validity of the index, I dropped any observation with more than 5 (out of 10) missing responses in this part, which lead to a reduction in sample size. The final score ranged from 10 to 40, with higher score indicating more severe depression conditions.

3.4. Variables
Measures of demographic variables, housing characteristics, physical health status as well as health behavior are included in the model. Negative events and life stressors are counted and used as instrumental variables in certain models.
Demographic variables include age, years of schooling, times of marriage, current
Hukou status, number of living biological children, number of passed biological children. Few (less than 5) missing values about schooling, current Hukou status are imputed. It is believed that shared resources might lead to depression concordance (Meyler, Stimpson, and Peek, 2007), so I include housing characteristic variables in the model to control for it. Housing characteristics include seven items about housing facilities to measure living condition. These seven dummies are accessibility of electricity, running water, shower or bath facility, natural gas supply, heating, telephone and broadband Internet connection.
Consistent with previous research on the mental health concordance of couples, I control for the respondents’ physical health status. Four dimensions of physical health are measured, namely disability, chronic disease, difficulty in daily activities (ADL) and cognitive function. Disability is measured as a dummy variable that equals to 1 whenever there is any disability in the following: physical disability, brain damage/mental retardation, vision problem, hearing problem and speech impediment. ADL is also a dummy variable capturing any difficulty in daily activities in the standard ADL questionnaire. The number of self-reported chronic disease measures chronic disease. For cognitive function, I calculated a cognitive function score based on the questions asked in the Cognitive and Depression section. The score is standardized to range from 0 to 10, with higher score indicating worse cognitive

5function. Besides, I use self-rated health as a measurement of subjective feeling about health conditions. The answer is an ordered scale from 1 (very good) to 5 (very poor). For health behavior, dummies about whether smoke or drink alcohol are included.
The negative event is defined as the recent death of close relatives. I classify negative events into two types: those common to both parties called shared and those whose primary impact only affects one particular individual named private. Shared negative events include the death of biological/adopted children with the current spouse in the recent one year. Private negative events include the death of own biological parent and additional biological children in the recent one year. Notice that the distinction between shared and private is rather arbitrary. The death of spouse’s parent may also have strong emotional impact on the respondent, especially when they used to live together. Life stressor catches the number of diagnoses of chronic disease in the recent one and a half years. Recent retirement (also within one and a half year) is treated independently as a life stressor.
Table 1a, Table 2a and Table 3a in the Appendix contain the summary statistics of the above variables in different samples.

4.Evidence for depression concordance

4.1. Correlation analysis 4.1.1. Spearman rank correlation coefficients

In the sample, the depression score is not normally distributed (See Appendix
Graph 1a). The skewness and kurtosis tests for normality of the depression scores reject the null hypotheses of normality with p-value<0.000. Therefore, I use Spearman rank correlation coefficients to do the preliminary correlation analysis. Spearman rank correlation is a nonparametric measure of statistical dependence between two variables, therefore is distribution free.
It also remains an interesting question of whether changes in depression symptoms are correlated within couples. To investigate into this, I use the short panel data and calculate the correlation between changes in depression scores.

4.1.2. Adjusted correlation
Adjusted correlation intends to control for confounding factors in correlation analysis. It has been used in clinical research to study couple similarities for cognitive functions and psychological health (Dufouil and Alpérovitch, 2000). The basic idea comes from residual analysis (Cook and Weisberg, 1982). In calculation, first I do a multiple linear regression to get coefficient estimation for each control variables. In the prediction regression, I control for potential variables that may affect depression symptom, including demographic variables, physical health status, health behavior, negative events and housing characteristics. The model is as follows:

ꢀꢁꢂꢃꢁꢄꢄꢅꢆꢇꢈ = ꢉꢊꢀꢁꢋꢆꢌꢃꢍꢂℎꢅꢎꢄꢈ + ꢉꢏꢐꢁꢍꢑꢒℎꢈ + ꢉꢓꢔꢁℎꢍꢕꢅꢆꢃ + ꢉꢖꢗꢕꢁꢇꢒꢄꢈ

ꢈ

+ ꢉꢘꢐꢆꢙꢄꢅꢇꢌꢚ + ꢛ

Where ꢅ denotes individual and ꢜ the household. Based on the coefficient estimation, I get the predicted scores for each observation.

  • ̂
  • ̂
  • ̂
  • ̂

̂
ꢀꢁꢂꢃꢁꢄꢄꢝꢆꢇꢞ = ꢉꢊꢀꢁꢋꢆꢌꢃꢍꢂℎꢅꢎꢄꢈ + ꢉꢏꢐꢁꢍꢑꢒℎꢈ + ꢉꢓꢔꢁℎꢍꢕꢅꢆꢃ + ꢉꢖꢗꢕꢁꢇꢒꢄꢈ

ꢈ

̂

+ ꢉꢘꢐꢆꢙꢄꢅꢇꢌꢚ

6
The residual, the difference between actual score and the predicted score, is defined as adjusted scores.

̂
ꢟꢠꢜꢙꢄꢒꢁꢠ ꢄꢎꢆꢃꢁꢄꢈ = ꢀꢁꢂꢃꢁꢄꢄꢅꢆꢇꢈ − ꢀꢁꢂꢃꢁꢄꢄꢝꢆꢇꢞ

The adjusted score theoretically contains potential omitted variables as well as concordance effect. Therefore the correlation between adjusted scores can be viewed as measurement of concordance after controlling for confounding variables, though it may run the risk of exaggerating the concordance.

4.2. Results 4.2.1. Spearman rank correlation coefficients

Table 1 shows the correlation coefficients.
Table 1. Spearman rank correlation coefficients of depression scores between couples Sample 2011 2011 2011 2011 2011 2012 2012 2012 2012 2012
Marriage duration All 0-29 30-39 40-49 50 and above All 0-29 30-39 40-49
Obs 5904 805 2333 1680 1086 784 41
Spearman's rho 0.435 0.435 0.409 0.421 0.454 0.438 0.410 0.383
Prob>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.000 0.000
303 235 205
0.443

  • 0.503
  • 50 and above

S ource: Author’s calculation based on CHARLS 2011,2012

From the correlation coefficients, we could gain a preliminary sense of depression concordance within Chinese mid-aged and elderly couples. All the coefficients are strongly positive and statistically significant. As the marriage duration gets longer, there is no consistent pattern of the change in correlation coefficients.
In the sample, the changes in depression score are normally distributed, thus I use
Pearson correlation coefficients instead. Table 3 shows the correlation between changes in depression scores during 2008-2012.

Table 3. Spearman rank correlation coefficients of changes of depression scores between couples

Sample Panel Panel Panel Panel Panel
Marriage duration All 0-29 30-39 40-49
Obs 478 26 205 139 108
Correlation 0.360 0.171 0.375 0.382
Prob>|t| 0.000 0.403 0.000 0.000

  • 0.000
  • 50 and above
  • 0.314

S ource: Author’s calculation based on CHARLS 2008,2012

From the table, we can see that except for the group of couples whose marriage duration is less than 30 years, which only contains limited observations, all other groups shows strong and significant correlation between changes in couples' depression symptoms during the four years between 2008 and 2012.

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    PREGNANCY AND INFANT LOSS RESOURCES “We Weep With Those Who Weep” (Romans 12: 15) To access links in this document, go to https://www.emfgp.org/resources-for-pregnancy-and-infant-loss-8-19/ Reflections for Families and Professionals (From the Archdiocese of Dubuque, IA) ❖ Reflection for Fathers ❖ Reflection for Mothers ❖ Reflection for Couples ❖ Reflection for Young Siblings ❖ Reflection for Older Siblings ❖ Reflection for Grandparents ❖ Reflection for Medical Professionals ❖ Reflection for Lay Ministers Catholic Blessings and Rituals ❖ Blessing of Parents After Miscarriage or Stillbirth ❖ Order for the Naming and Commendation of an Infant Who Died Before Birth – Archdiocese of St. Louis ❖ Naming Ceremony of a Child Lost to Miscarriage or Stillbirth – Diocese of Fargo Catholic and Christian Resources ❖ Emmaus Ministry for Grieving Parents ➢ A Catholic ministry serving the spiritual needs of parents whose children of any age (from conception to old age), who have died by any cause, no matter how long ago. Open to grieving parents of any religion. 1 ❖ Morning Light Ministry ➢ A Catholic ministry for mothers and fathers who have experienced the death of their baby. Also welcomes bereaved parents of other Christian denominations, other faiths, and of no religious affiliation who are struggling with the very notion of faith. ❖ All Embrace ➢ For families who have lost a baby. Provides resources and education to extended family, friends, religious leaders, medical personnel, and volunteers. ❖ Catholic Miscarriage Support ➢ Practical and spiritual
  • Does Hope Mediate and Moderate the Relationship Between Happiness Aversion and Depressive Symptoms?

    Does Hope Mediate and Moderate the Relationship Between Happiness Aversion and Depressive Symptoms?

    Open Journal of Depression, 2020, 9, 1-16 https://www.scirp.org/journal/ojd ISSN Online: 2169-9674 ISSN Print: 2169-9658 Does Hope Mediate and Moderate the Relationship between Happiness Aversion and Depressive Symptoms? Rebecca A. Bloore1* , Paul E. Jose1, Mohsen Joshanloo2 1Victoria University of Wellington, Wellington, New Zealand 2Keimung University, Daegu, South Korea How to cite this paper: Bloore, R. A., Jose, Abstract P. E., & Joshanloo, M. (2020). Does Hope Mediate and Moderate the Relationship The literature is beginning to document how and for whom “fear of happi- between Happiness Aversion and Depres- ness” (happiness aversion, i.e. the expectation that being happy can have sive Symptoms? Open Journal of Depres- negative consequences) is predictive of psychological outcomes. We sought to sion, 9, 1-16. https://doi.org/10.4236/ojd.2020.91001 determine whether hope, an important protective factor against depressive symptoms, might mediate and moderate the relationship between happiness Received: December 23, 2019 aversion and depression. In a dataset of 588 undergraduate psychology stu- Accepted: January 18, 2020 Published: January 21, 2020 dents, evidence was found that hope functioned as a mediator as well as a buffer in the relationship between happiness aversion and depression. In ad- Copyright © 2020 by author(s) and dition, exploratory analysis of a small longitudinal dataset (N = 74) suggested Scientific Research Publishing Inc. that hope also played the same roles in the relationship between fear of hap- This work is licensed under the Creative Commons Attribution International piness and depression over time. These findings suggest that interventions License (CC BY 4.0).
  • Melancholia Before the Twentieth Century: Fear and Sorrow Or Partial Insanity?

    Melancholia Before the Twentieth Century: Fear and Sorrow Or Partial Insanity?

    MINI REVIEW ARTICLE published: 03 February 2015 doi: 10.3389/fpsyg.2015.00081 Melancholia before the twentieth century: fear and sorrow or partial insanity? Diogo Telles-Correia* and João Gama Marques University Clinic of Psychiatry and Psychology, Faculty of Medicine, University of Lisbon, Lisboa, Portugal Edited by: Throughout the history of psychopathology, several meanings have been assigned to the Anne Giersch, Institut National de la term melancholia. The main ones were related to affective disorders (fear and sadness) Santé et de la Recherche Médicale, France and abnormal beliefs. At the time of Hippocrates melancholia was regarded mainly in Reviewed by: its affective component. Since that time, and until the eighteenth century, authors and Mark Elliott, National University of opinions have been divided, with both aspects (affective disorders and abnormal beliefs), Ireland Galway, Ireland being valued. Finally, in the eighteenth to nineteenth centuries, with Pinel at its peak, Alexandre Todorov, Washington melancholia becomes exclusively a synonym of abnormal beliefs. At the turn of the University School of Medicine, USA nineteenth to the twentieth century, the affective component returns as the main aspect Rebekka Lencer, University of characterizing melancholia. Muenster, Germany *Correspondence: Keywords: melancholia, depression, psychopathology, history, eighteenth century, history, nineteenth century Diogo Telles-Correia, University Clinic of Psychiatry and Psychology, Faculty of Medicine, University of Lisbon, Hospital Santa Maria, Avenida Egas Moniz, Lisboa 1649-028, Portugal e-mail: [email protected] INTRODUCTION FROM HIPPOCRATES TO THE EIGHTEENTH CENTURY Throughout the history of psychopathology, many terms have Hippocrates (460–379 BC) attributed the melancholic state been introduced. Some have disappeared, as “Phrenitis” and (as well as other disorders such as dysentery, skin rashes, etc.) “Carum,” while others have survived (Berrios, 1996a).
  • Coping with Sorrow, Loss and Grief

    Coping with Sorrow, Loss and Grief

    Coping with sorrow, loss and grief The term “Grief” is used to describe reactions and feelings that a person might have to the loss of someone or something that is important to them. The feeling of loss not only covers the immediate loss of a relationship, good health, a job, a way of life or the death of a loved one, but also includes the loss of generations of family bonds, the removal of links to spiritual land rights or neglect of the cultural heritage. What is grief? How long does grief last? Grief is not an illness – it can not be cured or You can’t put a time limit on hurried along. No two people experience grief in grief. The best answer seems to the same way. It is affected by many factors such as be “as long as it takes”. Grief the nature of the loss, the person’s past history, their doesn’t occur in neat stages. It cultural and spiritual beliefs, and their personality. is a chaotic process, which is There is no right or wrong way to experience grief. different for each person. Some common grief reactions include: We don’t recover from grief as • Sadness, crying we might from some physical • Shock, numbness illness. Instead, we gradually learn to adjust to the loss. This • Difficulty accepting the loss adjustment process depends on • Anger, guilt, shame how you express your grief and other feelings, • Isolation, alienation, loneliness your life experience, previous losses, coping style, • Relief personality, physical health and available support • Fear that reactions may not be normal systems.
  • Grief Resources

    Grief Resources

    Grief Resources If you need immediate support, please call First Candle: 1-800-221-7437 (Counselors are available 24/7) MISCARRIAGE, STILL BIRTH AND FULL TERM LOSS First Candle No matter how deep your Grief and how Great your pain, remember that you are not alone. First Candle, alonG with others who have traveled this road before you, can help you throuGh this difficult time. We can guide you through the grieving process and empower you to help other families facinG the same traGedy. For Immediate Grief Support, Call 1-800-221-7437. Counselors are available 24/7 Born Still Christian support for GrievinG parents Common Care Loss Support Common Care Loss Support is a support Group for those who have suffered miscarriaGe, stillbirth or early infant death and for those who experience pregnancy after loss. Registered n Jessamine County, Kentucky, we offer monthly support Group meetinGs and host special events and fundraisers. Count the Kicks Available as an app, Count the Kicks is a safe and simple way to help monitor baby’s well-being. It teaches expectant mothers how to monitor and track their baby’s movements daily in the third trimester of preGnancy with the Goal of reducinG stillbirth. They have a great pamphlet for parents and families who have experienced the death of both or all of a set of twins, triplets or hiGher multiples, durinG preGnancy, at birth, or in infancy. http://www.climb-support.orG/pdf/bothall.pdf CLIMB- center for loss in multiple birth/ www.climb-support.orG A non-profit organization based in Anchorage, Alaska, serving families and others throughout the United States, Canada and beyond.