ISSN 2215-3535

Actualidades en Psicología, 37 (134), enero-junio, 2023, 117-133

DOI: 10.15517/ap.v37i134.46758

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Universidad de Costa Rica

A Bayesian Network Perspective on an Attributional Model

of Reactions Toward People with HIV

Atribuições de causalidade sobre as pessoas vivendo com HIV: perspectiva de uma

análise Bayesiana

Angelo Brandelli Costa

https://orcid.org/0000-0002-0742-8152

Felipe Vilanova

https://orcid.org/0000-0002-2516-9975

Fernando Martins de Azevedo

https://orcid.org/0000-0002-2155-5975

Gisela Steins

https://orcid.org/0000-0002-3745-2778

1,2

Programa de pós-graduação em Psicologia, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil

Departamento de Psicologia do Desenvolvimento e da Personalidade, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Fakultät für Bildungswissenschaften, Institut für Psychologie, Universität Duisburg-Essen, Essen, Germany

angelo.costa@pucrs.br

felipevilanova2@gmail.com

fernandoazevedo940@gmail.com

gisela.steins@uni-due.de

Recieved: May 17, 2021. Accepted: June 12, 2023.

Abstract. Objetive. Attributional theory has been widely studied to understand the overall perceptions regard-

ing people suering from negative events such as an HIV infection. The aim of the present study is to test the

overall attribution model and its inuence on the willingness to help, considering emotional reactions related

to an HIV-infected individual. Method. We used a Bayesian network to analyze the association between attri-

butions of causality (blame, responsibility, and control), willingness to help, and emotional reactions (anger and

sympathy) toward an HIV-infected patient. Three hundred and fty-eight individuals participated in the study.

Results. Using the overall model, we found two dierent results: Anger contributed to the cognitive processes

of attribution, and sympathy contributed to the behavioral willingness to help the patient.

Keywords. HIV; Social Stigma; Prejudice, Social Psychology

Resumo. Objetivo. A teoria de atribuição de causalidade tem sido amplamente estudada para compreender

percepções a respeito de pessoas que sofrem o impacto de eventos negativos em saúde como uma infecção

por HIV. O objetivo deste estudo é testar o modelo de atribuição e seu impacto em intenção de ajudar, con-

siderando as reações emocionais direcionadas à um indivíduo que vive com HIV. Método. Utilizamos um pan-

orama bayesiano para analisar a associação entre atribuições de causalidade (culpa, responsabilidade e con-

trole), intenção de ajudar e reações emocionais (raiva e simpatia) no que diz respeito a um paciente com HIV.

Trezentos e cinquenta e oito indivíduos participaram deste estudo. Resultados. A partir do modelo utilizado,

encontramos dois resultados diferentes: raiva contribuiu ao processo cognitivo de atribuição e a emoção sim-

patia contribuiu ao processo comportamental de intenção de ajudar.

Keywords. HIV, Estigma Social, Preconceito, Psicologia Social

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

118

INICIO METHOD RESULTS DISCUSSION REFERENCES

Introduction

Weiner et al. (1998) hypothesized that people

with HIV/AIDS are perceived as being in control of

their infection and thus blamed for it. It is most likely

the social fabric that sustains such perceptions and

HIV-related stigma. Stigma is dened as the social

process of identifying a person or a group of people

as inferior or less valuable based on specic features

(

Link & Phelan, 2001). Getting infected with HIV may

prompt this type of social rejection. People living with

HIV are regarded by most as, if not at fault, being at

least responsible for getting infected. Consequently,

they are subject to reprimand, anger, and hostility.

Nevertheless, researchers have found that peo-

ple react dierently to individuals infected with HIV

depending on how they contracted the infection.

For example, Graham et al. (1993) found that people

who became infected due to drug use and needle

sharing evoked considerably more anger and less

sympathy than those who got infected through

blood transfusions. The attribution model can ex-

plain those dierent reactions (

Heider, 1958; Dela

Coleta, 1982

). When people attribute the cause of

a disease to external factors – i. e., factors that the

individual cannot control – they tend to feel sympa-

thy for the person. Nonetheless, when people per-

ceive a disease as being caused by an individual’s

direct actions, they tend to blame them and feel an-

ger towards them (

Mantler et al., 2003). In a recent

Brazilian study, researchers tried to investigate such

attributional dierences. An experiment was con-

ducted on a sample of university students from the

healthcare eld and healthcare professionals from

Porto Alegre (Brazil) to identify if causal attributions

changed in dierent infection scenarios (Azevedo,

et al., 2020

). The present study aims to replicate

the Brazilian experiment in a German sample. We

aim to investigate if blame still plays a role in the

perception of HIV-infected people according to the

attributional model and to analyze the association

between causal attribution, willingness to help, and

emotional reactions. Moreover, one of the objecti-

ves of this study is to compare control, responsibili-

ty, blame attributions, emotional reactions, and the

willingness to help heterosexual, homosexual, and

transgender women in scenarios of HIV infection

due to blood transfusion and unprotected sex.

Attributional Theories

According to attributional theories, people tend

to nd explanations for what they see. An attribu-

tion is an explanation about something (

Heider,

1958

). By nding meaning for the events that oc-

cur, people feel they can foresee what will happen

(

Dela Coleta, 1982). If someone’s attribution for skin

cancer is “people get skin cancer because they do

not wear sunscreen”, they will most likely think that

wearing sunscreen prevents skin cancer. However,

while individuals may seek reasoning for their and

others’ behaviors, their explanations are not always

logical. Attributions have a biased logic. They are

generally based on an individual’s own experiences

(

Dela Coleta & Dela Coleta, 2006; 2011).

As conceived by Weiner, seeing people as judges

and life as a courtroom is a guiding metaphor in

attributional theories. This notion conforms to the

central thesis of Weiner’s attributional framework,

in which the perceived causes of a given event, es-

pecially locus and controllability, are associated with

moral beliefs, inducing inferences about an indivi-

dual’s responsibility for the event. The perception of

responsibility is related to moral emotions, such as

anger or sympathy, and associated with willingness

to help versus aggression. Thus, Weiner (

2006, p.

43) states that moral beliefs are signicant guiding

judges of events, prompted by concepts such as

“sickness versus sin” and “good versus evil”.

Attribution of responsibility is usually inuenced

not only by an individual’s personal thoughts and

emotions but also by cultural features (

Steins &

Weiner, 1999

). Therefore, responsibility attribution

diers within collectivist and individualist cultures. In

contrast to individualist countries, such as Germany,

in collectivist countries, such as Brazil (Hofstede,

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

119

INICIO METHOD RESULTS DISCUSSION REFERENCES

ritarianism, hostility towards homosexuals, belief in a

just world, and their concern for equality and social

justice). The authors found support for the controlla-

bility-responsibility-blame judgment sequence. Ra-

tings were higher for the active vignettes; however,

there was no dierence depending on the disease.

Using a regression path model,

Mantler et al. (2003)

revealed that pre-existing social attitudes may have a

determining impact on blame, and blame inuences

behavioral intentions. Here,

Mantler et al. (2013) are

referring to Alicke’s culpable control model of blame

(

Alicke, 2000). In this model, pre-existing attitudes are

associated with judgments of blame, independently

from the cognitive and rational sequences advanced

in the decision-stage models.

Attributional Framework and HIV

In addition to research that measured attributio-

nal tendencies in serious diseases in general (

Cran-

dall & Moriarty, 1995

; Mantler et al., 2003), some

studies focused on the attributional framework of

HIV/AIDS.

Dooley (1995) tested attributions regar-

ding an AIDS diagnosis with a sample of 255 so-

ciology students. The experiment involved a man

that had just been diagnosed with AIDS. There

were ve possible scenarios for the HIV infection:

homosexual partner, heterosexual partner, blood

transfusion, injected drug use, and unspecied cau-

se. Results showed that sexual transmission (both

heterosexual and homosexual), transmission via

drug use, and unspecied transmission produced

higher attributions of control and higher feelings

of anger. The blood transfusion scenario resulted in

fewer attributions of control and increased sympa-

thy. Information about the transmission seems to

be a signicant factor in the attributional process.

Drug users may be more stigmatized in comparison

to individuals in need of blood transfusions (

Ander-

son, 1992

; Weiner et al., 1988).

In another study, it was found that gender is

also an important factor in attributional processes.

Cobb and De Chabert’s (2002) experiment involved

a person who sought counselling for their recent

2001), compassion tends to play a more signicant

role in responsibility attribution (

Levine et al., 2001).

The attributional framework has been applied

for study in dierent areas, such as school-related

issues, marital distress, communication with consu-

mers, and reactions to others with physical health

problems – as is the case in the present study.

Wei-

ner (2006)

hypothesizes that the dissemination of

such a framework may help reduce discrimination

against ill patients. If healthcare professionals do

not see their patients as responsible for getting sick,

they will sympathize with them rather than reject

them (

Weiner, 1995; Crandall & Moriarty, 1995).

Mantler et al. (2003) made another elaboration

on the attributional model. The authors address

some concepts introduced in

Alicke’s (2000) cul-

pable control model regarding blame.

Mantler et

al. (2003)

stress inconsistent results concerning dis-

tinctions between controllability, responsibility, and

blame. According to these authors, it was unclear

whether controllability, responsibility and blame

judgments dier systematically in response to ne-

gative events. They assumed sequence judgments

varied between controllability-responsibility-bla-

me, guiding a decision-stage model impacted by

pre-existing attitudes, as formulated by

Heider

(1958)

, Weiner (1995), or Shaver (1985).

To investigate the sequence proposed and the

inuence of pre-existing attitudes regarding this se-

quence on emotions, willingness to help, and social

contact, the authors studied the self-reported be-

havior of male students. Vignettes displaying “Wi-

lliam” as a target varied between two types of disea-

se (lung cancer and HIV) and two levels of agency:

active (smoker, infected due to drug use, infected

due to gay unsafe sex) versus passive (non-smoker,

infected by blood transfusion, due to mother-to-in-

fant contamination). Participants were asked several

questions to measure their attributions (judgments

of controllability, responsibility, and blame), their

behavioral intentions (personal willingness, support

for institutional help, social distance), their emotions

(anger, sympathy), and their social attitudes (autho-

Actualidades en Psicología, 37(134), 2023.

120

INICIO METHOD RESULTS DISCUSSION REFERENCES

Attributional Model of Reactions Toward People with HIV

HIV diagnosis. Gender was the only variable that

changed. The attributions of responsibility were sig-

nicant for both women and men. However, partici-

pants displayed a higher willingness to help women

(Cobb & De Chabert, 2002).

Further, S

eacat et al. (2007) tested the attributio-

nal framework of control-responsibility-blame with

HIV scenarios that also accounted for dierences

between heterosexual and homosexual men. The

results of this experiment indicate signicant attri-

butions of control, responsibility, and blame for all

scenarios of infection. Control and responsibility

attributions were higher if the person was homo-

sexual (

Seacat et al., 2007).

Therefore, there is sucient scientic eviden-

ce to support the investigation of these attribu-

tion tendencies. Negative attributions may make

people feel uncomfortable accessing health ser-

vices (

Costa et al., 2018), which can worsen the

HIV epidemic. Blame, for instance, is an obstacle

to HIV serological status disclosure, which is often

important for HIV-infected people as it increases

their quality of life (

Paxton, 2000). Blame can be

associated with the misconception of preventive

programs (

Maes, 1994). Self-blame and blame at-

tributions in general may hinder self-care and hel-

ping behaviors (

Cobb & De Chabert, 2002). Con-

trol-blame-responsibility attributions most likely

still inuence how these patients are perceived

by healthcare sta. However, at the time former

studies about attributional frames regarding HIV

were conducted, there were no eective medica-

tions to treat the disease. Nowadays, there are

dierent treatment possibilities, and the infection

is no longer necessarily fatal. Thus, considering

the detrimental implications that attributions may

have on the health of individuals and the new con-

text of the HIV/AIDS epidemic, it is important to

conduct scientic investigations about the contro-

llability-responsibility-blame attributional sequen-

ce. This is the aim of the present study.

In previous researches, the attributional theory

was tested through data analysis techniques that

require a priori input of the model structure. Linear

regression was the most used technique; it requi-

res a priori specication of independent and de-

pendent variables. Even though linear regression

provides an eective method for testing hypothe-

ses, it hinders the possibility of nding alternative

attributional paths that have not yet been establi-

shed. In the present study, we attempted to use a

novel data analysis technique that does not requi-

re a priori input of the model structure to analyze

the relationship between attributions of causality

(blame, responsibility, and control), willingness to

help, and emotional reactions (anger and sympa-

thy). We aimed to assess whether the structure

developed without a priori inputs on independent

and dependent variables from a Bayesian network

associates controllability, responsibility, blame,

and positive and negative emotions with behavio-

ral intentions.

Method

The original study (Azevedo, 2000) used the ex-

perimental vignette from the study by

Seacat et al.

(2007)

, which was translated into Brazilian Portu-

guese and German (from the Brazilian Portuguese

version). The vignette depicted a situation of HIV in-

fection. The person in the scene was either a hetero-

sexual man, a homosexual man, or a trans woman,

and the infection necessarily occurred in a situation

of unprotected sex or blood transfusion. The six pos-

sible scenarios were: (1) heterosexual man, unprotec-

ted sex; (2) heterosexual man, blood transfusion; (3)

homosexual man, unprotected sex; (4) homosexual

man, blood transfusion; (5) trans woman, unprotec-

ted sex; and (6) trans woman, blood transfusion. The

scenarios with the heterosexual man can be consi-

dered a control group, while the rest are the expe-

rimental groups. For this study, variations were not

considered in terms of sexual orientation and gen-

der identity, nor the forms of exposure. A validation

question asked for basic information about the vig-

nette to exclude participants who did not understand

or read the text provided.

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

121

INICIO METHOD RESULTS DISCUSSION REFERENCES

an HIV infection. The person in the scene was either

a heterosexual man, a gay man, or a trans woman,

and the culprit of the infection was either unprotec-

ted sex or a blood transfusion. The six possible sce-

narios were as follows: (1) heterosexual man, unpro-

tected sex; (2) heterosexual man, blood transfusion;

(3) gay man, unprotected sex; (4) gay man, blood

transfusion; (5) trans woman, unprotected sex; and

(6) trans woman, blood transfusion. The scenarios

that included the heterosexual male can be consi-

dered a control group, while the others are the ex-

perimental groups. For this study, neither variations

in terms of sexual orientation and gender identity

nor the routes of exposure were considered. A vali-

dation question requested basic information about

the vignette with the aim of excluding participants

who had not understood or read the provided text.

Sociodemographics

Participants answered sociodemographic ques-

tions about gender, age, sexual orientation, profes-

sion, and religion.

Attribution of control, blame, and responsibility

The scale that measured participants’ attribution

to the vignette had a total of 13 items (e. g., “The

person in the scene is responsible for his disease”;

Mantler et al., 2003), answered in a ve-point Li-

kert scale. The answers could range from 1 (totally

agree) to 5 (totally disagree). The items assess the

extent to which the participant attributes the possi-

bility of control, responsibility, and blame for the di-

sease to the person in the scene. Seacat

et al. (2007)

found acceptable internal consistency indices for

the subscales responsibility (α = .91), control (α =

.88), and blame (α = .84). For this study, Cronbach’s

alpha values were .88, .90, and .79, respectively. The

three-factor model showed good adjustments in

the CFA with the DWLS estimator: CFI = .99; TLI =

.99; RMSEA = .04 (90% C.I. [.03, .06]).

Emotional reactions

The eight-item scale of emotional reactions was

also developed by Mantler et al. (2003). Of the ei-

Participants

The participants were undergraduate students

from the medicine, nursing, dentistry, psychology,

and physical education courses, and health profes-

sionals from the city of Porto Alegre, Brazil, and stu-

dents from the nursing, psychology, social pedago-

gy courses and medical professionals from the city

of Essen, Germany. In regard to the Brazilian parti-

cipants, the course coordinators from Universidade

Federal do Rio Grande do Sul (UFRGS) sent e-mails

to the students to invite them to participate in the

research. The same process was carried out by the

district oces of the Municipal Health Department

of Porto Alegre regarding the health service sta.

The sample of German participants was obtained

through convenience sampling. Participants acces-

sed the research website through advertisements

posted on social networks or leaets distributed at

the Universität Duisburg-Essen.

Nine individuals did not consent to the study.

Thus, they did not take part in data collection. One

hundred and two individuals left incomplete proto-

cols, and forty-nine were excluded because they did

not respond correctly to the manipulation-check

questions. Therefore, the data analysis included

three hundred and fty-eight individuals from 18

to 75 years old (M = 26.12; SD = 8.91). 82.4% of

them were female, 71.23% (n = 255) from Brazil, and

28.77% (n = 103) from Germany.

Ethics Statement

The investigation was approved by the ethics

committee of the Brazilian and German universities

with which the research is associated, in accordance

with Helsinki principles. All participants digitally sig-

ned an Informed Consent Form.

Instruments

The original study

(Azevedo, 2000) used the ex-

perimental vignette from

Seacat et al.’s (2007) inves-

tigation. The vignette was translated into Brazilian

Portuguese and German (from the Brazilian Portu-

guese version). The vignette depicted a situation of

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

122

INICIO METHOD RESULTS DISCUSSION REFERENCES

ce, were taken into consideration. As content validi-

ty evidence, the translators agreed that all aspects

assessed were clear.

Data collection was conducted between April

2017 and October 2018 by using online forms. Befo-

re completing the questionnaire, participants signed

an Informed Consent Form. Anonymity was guaran-

teed, and only the researchers had access to data.

The instruments were presented in the following or-

der: 1) emotional reactions; 2) attribution of control,

blame, and responsibility; and 3) willingness to help.

The average time for completion was ≅ 17 minutes.

Data Analysis

The three scales were calculated using mean sco-

res. We used a two-step approach: rstly, the equi-

valence class was estimated using the EBICglasso;

subsequently, the alternative models were tted. The

present technique consists of a Bayesian network vi-

sualized as a Directed Acyclic Graph, which is a me-

thod that suggests one structure of path connections

based on the robust associations between all varia-

bles inserted in a model (

Nagarajan et al., 2013).

Zero-order Pearson Correlations and gLasso Network

Zero-order Pearson correlations were calculated

between all variables to assess the magnitude of

their associations. Afterward, a partial correlation

network was conducted with the R software ver-

sion 1.1.442, using the package “qgraph” (Epskamp

et al., 2012

). In a partial correlation network, each

node represents one of the variables inputted, and

each edge represents partial associations between

variables. The model is known as Pairwise Markov

Random Fields, estimated through L1-regularized

neighborhood regression. The regularization is

obtained through the Least Absolute Shrinkage

and Selection Operator (LASSO;

Friedman et al.,

2008

) that controls model sparsity. This network

allows visualization of the partial correlations be-

tween variables. Positive correlations are repre-

sented by blue edges, while negative correlations

are represented by red edges. The strength of the

ght items in the scale, four pertain to the extent

to which each participant feels angry at the per-

son in the scene (e. g., “I feel angry at the person

in the scene”); the remaining four items measure

sympathy (e. g., “I feel compassion for the person

in the scene”). Answers range from 1 (totally agree)

to 5 (totally disagree). In its original development

sample, this scale had an acceptable internal con-

sistency of α = 0.71 (

Seacat et al., 2007). The current

study had an internal consistency of .60 for positive

emotions and .50 for negative emotions. The scale

had an overall internal consistency of α = .56, 95%

C.I. = (.50, .62). By conducting the CFA using the

DWLS estimator, we found good adjustment for this

sample in a two-factor solution: CFI = .99; TLI = .98;

(RMSEA = .04 (90% I.C. [.01; .07]).

Willingness to help

Willingness to help the user was evaluated by

using a 10-item scale developed by

Dooley (1995).

The 10 items on the scale (e. g., “I would help the

person in the scene to walk,” “I would go to the

pharmacy to ll a prescription for the person in the

scene”) specically assess the willingness to help

people living with HIV or AIDS in a ve-point Likert

scale. Answers range from 1 (totally agree) to 5 (to-

tally disagree). The scale had an internal consisten-

cy of α = .88 (

Seacat et al., 2007). For the present

study, Cronbach’s α was .92. The CFA conducted by

using the DWLS estimator for a single-factor solu-

tion showed good t indices: CFI = .99; TLI = .99;

RMSEA = .03 (90% C.I [.00, .05]).

Procedures

The Portuguese-German adaptation of the ins-

truments was conducted by an expert native Brazi-

lian translator who is uent in German. Subsequent-

ly, a German social psychologist and a Brazilian

social psychologist uent in German evaluated the

translation considering its comprehension, format,

and instructions. Relevant aspects pertaining to the

cross-cultural validation of psychological instru-

ments, such as conceptual and idiomatic equivalen-

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

123

INICIO METHOD RESULTS DISCUSSION REFERENCES

learn” package is the log-likelihood loss – i.e., the

expected negative log-likelihood. Thus, the lower

the expected loss value, the better.

The DAG only provides unstandardized regres-

sion weights. Therefore, a path analysis reprodu-

cing the model generated by the DAG was con-

ducted to assess the standardized Beta coecients

of the connections discerned by the algorithm.

This analysis was carried out by using the Robust

Maximum Likelihood extraction method in the “la-

vaan” package (

Rosseel, 2012).

Results

Table 1 shows the zero-order Pearson corre-

lations between attributions of causality (control,

blame, and responsibility), willingness to help, and

emotional reactions (anger, sympathy). Attributed

responsibility had statistically signicant correla-

tions with willingness to help (r = -.23, p < .001),

sympathy toward the infected person (r = -.84, p <

.001), and attribution of blame (r = .11, p = .035). At-

tribution of control had signicant correlations with

anger towards the infected person (r = .91, p < .001),

sympathy towards the infected person (r = -.18, p

< .001), and attribution of blame (r = .40, p < .001).

Attribution of blame had signicant correlations

with anger towards the infected person (r = .35, p

< .001) and sympathy towards the infected person

(r = -.27, p < .001). Finally, sympathy towards the

infected person signicantly correlated with willing-

ness to help (r = .30, p < .001) and anger towards

the infected person (r = -.15, p = .004).

The thickest edge in the gLasso network is the

one connecting attribution of control and anger

towards the infected person - same as in zero-or-

der Pearson correlations, i.e., the connection with

the strongest magnitude. The second thickest edge

connects attribution of responsibility and sympathy

towards the infected person. Thus, the strongest as-

sociations found in zero-order Pearson correlations

were also found in partial correlations, as depicted

in the gLasso network in

Figure 1. Furthermore, the

expected inuence of each node was also asses-

associations is represented by the thickness of the

edges – thus, the thickest the edge, the stronger

the association. The inputted variables were the

mean scores of the instruments, which assess the

constructs “Willingness to help,” “Emotional reac-

tions” (Sympathy and Anger), and “Attribution of

control, blame, and responsibility.” The Expected

Inuence of the inputted variables was also pro-

vided – i.e., the cumulative expected role of each

node in the activation, persistence, and remission

of the network (

Robinaugh et al., 2016). We deci-

ded to assess Expected Inuence as the measure

of centrality because it considers negative asso-

ciations among nodes to indicate the importan-

ce of each node in a network (Robinaugh et al.,

2016

). Other centrality measures, such as Strength,

do not take the negative associations into account,

providing a limited metric instead.

The gLasso network provides undirected graphs

– i.e., only the magnitude of the partial correlation

between variables is represented. The directionality

of the associations is not estimated, and, therefore,

a Bayesian Network analysis was conducted.

Directed Acyclic Graph

To compute a Bayesian network, displayed as a

Directed Acyclic Graph (DAG), we inputted the same

variables as in the gLasso network and ran the Tabu

algorithm in the R software version 1.1.442 using the

package “bnlearn” (

Scutari, 2010). The direction of

the associations between variables is estimated in

causal probability estimates (

Nagarajan et al., 2013)

by using the Tabu algorithm to estimate the direc-

tion of the causal relations. A k-fold cross-validation

procedure using the Tabu learning algorithm and

the Robust Maximum Likelihood extraction method

was conducted, and the expected loss value was

considered. It is worth highlighting that k-fold is a

common cross-validation method to estimate how

the model is expected to perform in other datasets

by shuing the dataset randomly, splitting it into k

groups, and assessing the adequacy of the model

in these groups. The loss function used in the “bn-

Attributional Model of Reactions Toward People with HIV

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INICIO METHOD RESULTS DISCUSSION REFERENCES

1 2 3 4 5 6

1. Willingness to help -

2. Anger -.09 -

3. Sympathy .30*** -.15** -

4. Blame -.07 .35*** -.27*** -

5. Control -.10 .91*** -.18*** .40*** -

6. Responsibility -.23*** .03 -.84*** .11* .05 -

Note. *p < .05, **p < .01 ***, p <.001

Table 1. Zero-order Pearson correlations between attributions of causality,

willingness to help, and emotional reactions (n = 356)

Control

Blame

Sympathy

Responsability

Help

Figure 1. DgLasso Network

Note. “Help” corresponds to “Willingness to help”, “Responsibility” to “attribution of responsibility”, “Bla-

me” to “Attribution of blame”, and “Control” to “Attribution of control.”

Attributional Model of Reactions Toward People with HIV

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INICIO METHOD RESULTS DISCUSSION REFERENCES

Figure 3 depicts the DAG that emerged from

the algorithm. The average Markov blanket size

and the average neighborhood size were 2.00, and

the penalization coecient, obtained through 133

tests in the learning procedure, was 2.94. A K-fold

cross-validation for Bayesian networks procedure

using 10 folds was conducted, and the expected

loss value was 5.48. It is worth noting that the 10-

fold cross-validation was selected since the para-

meter for k is between 5 and 10 (

Jung, 2017).

Some points are notable in the DAG. Firstly, an-

ger towards the infected person and attribution

of responsibility were exogenous variables, which

means that they were not predicted by any other

variable but predicted third variables: anger pre-

sed and displayed in

Figure 2, indicating that the

attribution of control and responsibility have the hi-

ghest cumulative inuence in the network.

Table 2 provides the partial correlation values

between all variables in the network. The partial

correlations support the strong association be-

tween attribution of control and anger against the

infected person (r = .89), as well as between attri-

bution of responsibility and sympathy towards the

infected person (r = -.84). It is important to stress

that, due to the regularization method used, par-

tial correlations whose values are not higher than

the threshold estimated through EBIC computa-

tion are set to 0 (for a thorough description, see

Epskamp & Fried, 2018).

Expectedlnﬂuence

Sympathy

Responsibility

Help

Guilt

Control

Anger

1.00

0.75

0.25

0.50

Figure 2. Expected Inuence of each variable in the network

Note. “Help” corresponds to “Willingness to help”, “Responsibility” to “attribution of responsibility”, “Bla-

me” to “Attribution of blame”, and “Control” to “Attribution of control.”

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INICIO METHOD RESULTS DISCUSSION REFERENCES

1 2 3 4 5 6

1. Willingness to help -

2. Anger 0 -

3. Sympathy .17 0 -

4. Blame 0 0 -.26 -

5. Control 0 .89 -.06 .16 -

6. Responsibility 0 0 -.84 .16 .04 -

Table 2. Partial correlation values represented in the gLasso network.

Willingness to help, and emotional reaction

Figure 3. Directed Acyclic Graph

Note. “Help” corresponds to “Willingness to help”, “Responsibility” to “attribution of responsibility”, “Bla-

me” to “Attribution of blame”, and “Control” to “Attribution of control.”

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INICIO METHOD RESULTS DISCUSSION REFERENCES

Figure 4. β coecients between the paths suggested by the DAG

Note. “Ajd” = Willingness to help; “Smp” = Sympathy; “Rsp” = Attribution of responsibility; “Clp” = Attri-

bution of blame; “Cnt” = Attribution of control; “Rav” = Anger towards the infected person.

dicted attribution of control, whereas attribution

of responsibility predicted sympathy towards the

infected person. Further, the willingness to help

was an endogenous variable – i.e., it did not pre-

dict any variable but was predicted by sympathy

towards the infected person. Attribution of con-

trol, attribution of blame, and sympathy towards

the infected person were predicted by third varia-

bles but also predicted other variables. For instan-

ce, attribution of control predicted attribution of

blame and sympathy towards the patient, whereas

attribution of blame predicted sympathy towards

the patient. Moreover, sympathy was the only pre-

dictor of willingness to help.

In regard to the standardized Beta coecients

obtained through the path analysis reproducing

the model that emerged from the DAG, all paths

were statistically signicant (p < .01) (Figure 4). The

path with the highest magnitude was anger pre-

dicting attribution of control (β = .91, p <. 001),

followed by the attribution of responsibility pre-

dicting sympathy towards the infected person (β

= -.84, p < .001), attribution of control predicting

attribution of blame (β = .40, p < .001), sympathy

towards the infected person predicting willingness

to help (β = .29, p < .001), and attribution of blame

predicting sympathy towards the infected person

(β = -.15, p < .001).

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INICIO METHOD RESULTS DISCUSSION REFERENCES

Discussion

The ndings of this study are consistent with pre-

vious researches that applied the attributional mo-

del in the context of HIV infection (

McDonell, 1993;

Dooley, 1995; Cobb & De Chabert, 2002; Mantler

et al., 2003

; Seacat et al., 2007). The results indicate

that attributions of blame, control, and responsibi-

lity play signicant but dierent roles in emotional

reactions and in the willingness to help. It is thus

worth stressing that our procedures support the

robustness of our inferences by not only providing

results that are consistent with previous studies on

causal attribution but also by using methods that

were used and pointed out as adequate in past re-

search (e. g.,

McNally et al., 2017).

One of the main merits of our study in compari-

son to previous literature was the demonstration of

the applicability of the model within a methodology

in which the relationship between the variables was

not assumed a priori. This nding provides further

validity evidence to the theory of the association be-

tween the attribution processes (separating the cog-

nitive components), the positive and negative emo-

tions, and, in turn, its inuence on the willingness to

help. Moreover, the present research approached

some unexplored issues – namely, that positive emo-

tions directly aected the intention to help, which is

inuenced by the attribution processes that are, in

turn, inuenced by the negative emotion (anger).

Therefore, a negative emotion towards a person with

HIV increases the tendency to attribute less control

regarding the infection, which will induce greater

blame, and, therefore, leads to less sympathy and a

decrease in the willingness to help. Conversely, the

tendency to attribute responsibility to individuals for

their HIV infection can also reduce feelings of sym-

pathy in a dierent path. Hence, our study demons-

trates the inuence of emotions in attributions and

behavior, displaying two distinct paths.

Therefore, the present study does not conrm the

linear association between variables based on the

control–responsibility–blame model. The cognitive

processes, emotional reactions, and behaviors do not

follow this linear attributional framework as well (

Hei-

der, 1958

; Shaver, 1985; Weiner, 1995) – at least not in

this combined sample. In contrast to

Mantler et al’s

(2003)

ndings, blame was not the nal attribution

in our survey. It is worth emphasizing that

Mantler

et al. (2003)

tested the directionality of the cognitive

process with a behavioral outcome, not considering

the role of emotions – an aspect that may have con-

tributed to the divergence of our results.

In Mantler et al. (2003), blame was the judgment

most strongly associated with behavioral responses.

Mantler and collaborators assumed that, in real-life

contexts, people might not necessarily go through

the stages of the decision-stage models but may

instead focus on the blameworthiness of the per-

ceived person, going backward through the stages

to seek conrmatory evidence for their hypotheses

and prejudices (

Alicke, 2000). Also, our study raises

the question of whether, under such circumstances,

control on the side of the perceiver is still important

and triggers the belief in a just world, victim bla-

ming, and other processes associated with blame.

According to Lerner’s hypothesis, people want to

preserve their belief in a just world by demeaning

or blaming victims, which presupposes that people

get what they deserve (

Lerner & Miller, 1978). Li-

kewise, the Defensive Attribution Hypothesis may

still explain behavior in this context – namely, that

people want to keep their belief in having personal

control over their own lives by considering the vic-

tims self-responsible.

By blaming victims for something that has ha-

ppened to them and attributing it to faults and ca-

relessness, people presuppose that this would not

happen to them (

Walster, 1966; Shaver, 1970). Mo-

reover, a belief in immanent justice will prompt a

need to blame the victim (

Berrenberg et al., 1990).

Conversely, our results point out that negative

emotions could be an index of prejudices that were

present before the cognitive-attributional process

occurred. A recent systematic review involving heal-

thcare professionals’ beliefs and attitudes towards

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

129

INICIO METHOD RESULTS DISCUSSION REFERENCES

is sustained evidence that the experience of stigma

can reduce HIV testing rates (

Turan et al., 2011), in-

crease avoidance of healthcare services (

Pachankis et

al., 2015

), and increase the frequency of unprotec-

ted sex (

Ayala et al., 2013). Therefore, if HIV-infected

people are held personally accountable, they will not

get social support, which is important when coping

with a serious illness (

Berrenberg et al., 1990).

There are, however, strategies that can be adop-

ted in order to reduce HIV-related stigma.

Stahlman

et al. (2017)

and Mahajan et al. (2008) recommend

that data regarding stigma should be included in

the HIV/AIDS routine surveillance in conjunction

with condom distribution, HIV testing, and antire-

troviral therapy. According to

Mahajan et al. (2008),

measuring stigma with adequate, valid, and reliable

instruments can help assess prevention and treat-

ment programs and help determine the ecacy of

stigma reduction interventions. In summary, to suc-

ceed in dealing with the HIV/AIDS epidemic, it is

necessary to implement measures that assess stig-

ma in the routine protocol for HIV/AIDS (

Stahlman

et al., 2017

) and to create high-quality stigma re-

duction interventions with multidimensional stigma

indicators (

Mak et al., 2017).

Taking into consideration the main aim of this

study, we conclude that the attributional process is a

key aspect in the perception of HIV-positive people

and that the status of the disease as curable might

not have changed how HIV-infected people are per-

ceived. Moreover, the control-responsibility-blame

sequence could display dierent congurations in

the presence of positive and negative emotions. Res-

ponsibility aects sympathy independently from an-

ger, and anger inuences control and blame. Blame

attribution still plays a role in our study, aected by

attribution of control – inuenced by anger, but sym-

pathy seems central to understanding willingness to

help, aected by the attribution of responsibility.

In addition, we can still expect self-stigmatiza-

tion from HIV-infected people. Evidence from attri-

butional theories contributes to our understanding

of how people who suered negative life events are

patients who suer from substance abuse disorders

found that, in most of the analyzed studies, health-

care sta tends to have negative emotions in regard

to such patients (

Van Boekel et al., 2013). Thus, in

our results, prejudice against an HIV-positive per-

son could prompt anger. This is consistent with

Co-

rrigan et al (2003)

study, which indicates that anger

and fear were highly correlated in the context of

the attributional process. Nevertheless, in both ar-

guments, the attribution of responsibility is emotio-

nally biased.

In Weiner’s attribution model, responsibility for

a behavior plays an essential part in the interaction

with sympathy, determining behavioral responses

to an event.

Weiner (1995) makes a clear distinction

between the attribution of control and responsibi-

lity. Control is related to a person’s degree of con-

trol over a behavior; responsibility is an evaluation

that involves morality. In our study, attribution of

responsibility decreased sympathy that, in turn,

reduced behavior intention - congruent with

Dag-

nan and Cairns (2005)

, who found an association

between positive emotions and the attribution of

responsibility and showed that sympathy was the

only independent predictor of helping behavior. In

accordance with our ndings,

Armstrong and Dag-

nan (2011)

found that, in the context of intellectual

disability, responsibility aected willingness to help

independently of anger. In addition, positive emo-

tions are favorable in explaining helping behaviors

in a specic manner. In a study conducted by

Sha-

rrock et al. (1990)

, optimism signicantly inuence

the willingness to help the paramedical and nursing

sta in a mental health facility that cared for people

in conict with the law. Further, there is evidence

that sympathy is a signicant factor in increasing

the willingness to help, and it is associated with

lower attributions of responsibility (

Dagnan & Cair-

ns, 2005

). Considering the role that responsibility

plays, it is inevitable to discuss our results within the

framework of stigma as well.

HIV-related stigma is one of the major factors as-

sociated with HIV infection (Logie et al., 2016). There

Attributional Model of Reactions Toward People with HIV

Actualidades en Psicología, 37(134), 2023.

130

INICIO METHOD RESULTS DISCUSSION REFERENCES

test attributional process models in the context of

HIV infection using data analysis techniques that do

not require a priori inputs to the model.

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