Transcript Differences between perceived vulnerability and perceived

Differences between
perceived vulnerability and
perceived risk:
Implications for health
theory and interventions
Jennifer J. Harman, PhD
Colorado State University
2005-2010
 Assistant Professor, Applied Social Psychology
Colorado State University
 Remained an affiliate of CHIP
 Got married and had 2 children
Harman, J. J., Wilson, K., & Keneski, E. (2010). Social and
environmental contributors to perceived vulnerability and perception
of risk for negative health consequences.
In J. G. Lavino & R. B. Neumann (Eds.),
Psychology of Risk Perception, pp. 1-45.
Hauppauge, NY: Nova Science Publishers,
Inc.
Background
 Risk perception for HIV infection
in intimate relationships
• Harman, Smith & Egan, (2007)
• Harman, O’Grady & Wilson (2009)
 Seemingly no differences in high
risk versus lower risk populations
• Harman, Wilson & Keneski (2010)
Background (cont.)
Information
Behavioral
Skills
Behavior
Motivation
Adapted from Fisher & Fisher, 1992
Background (cont.)
Information
Motivation
Behavioral
Skills
Behavior
Motivation
Attitudes
Social
Norms
Perceived
Vulnerability
Perceived vulnerability (PV)
versus Perception of risk (PoR)
 Terms have been used interchangeably in health
promotion/risk prevention literature
 Affect/feeling
• “ I feel vulnerable to getting HIV”
 Cognitive/beliefs
• “I think I am at high risk for getting HIV”
Now we know our ABCs…
 Affective attitudes
 Behavioral attitudes
 Cognitive attitudes
Two separate constructs
 Perceived Vulnerability (PV)
 Affective in nature
 Perception of Risk (PoR)
 Cognitive in nature
Health Behavior Theories and PV
 Health Belief model (Rosenstock, 1974)
 Protection Motivation Theory (Rogers, 1983)
 Extended Parallel Process Model (Witte, 1992)
Why should I care?
 Research support for PV as a predictor of
attitudes, intentions and outcomes is inconsistent.
 Simple health concerns: PV usually related
• E.g., adherence to a medical regimen following a sports injury
 Complex health concerns: less consistent
• E.g., genetic risk information for cancer
Development
PV
 Classical conditioning &
PoR
 Linkages between
other automatic associative
processes
acquired information and
attitude object
 E.g., fear-smoking
 E.g., beliefs about exercise-
diabetes
 Probability important
PV and PoR and health outcomes
Negative Relationship?
Positive Relationship?
 Defensive behavior activation  Protective behavior activation
 E.g., PV + condom use
 Optimistic biases (e.g., Lek & Bishop, 1995)
 Denial
So what is the problem?
 Health behavior change interventions often
introduce threats to increase PV or PoR
 If a defensive response is activated, this “threat”
may backfire
The measurement bugaboo
 PV and PoR measurements often combined or not
reported
 PV: affective measures/automatic associations
 IAT, facial expression instruments, physiological
reactions, cartoon face identification
 PoR: cognitive measures of beliefs
 Self-report
The intervention challenge
 Interventions manipulate specific variables to
create change in psychological and/or health
outcomes
 Social and environmental contributors to PV and
PoR proximal in nature
Social
Environmental
Changing PV
 Implicit attitude change
(Gawronski & Bodenhausen, 2001)
• Change how
associations are made
• E.g., associate a new feeling
with the behavior
• Social marketing
• Change activation of
pre-existing patterns of
associations
Changing PoR
 Explicit attitude change strategies
 Change in associative evaluation
• Gradual change of associative patterns lead to change in PoR
 Change in propositions relevant for judgments
• E.g., provide risk information
 Change in strategy to achieve consistency
• E.g., “It can happen to you” campaigns
Narrative Intervention Review
 MedLine and Psychinfo lit search
936 Total Citations
90 “eligible” articles
59 studies remained after through review
Strategies used
 76 intervention elements
 Vast majority targeted PoR
• 73% used second route of PoR change
• 15.4% used third strategy (e.g., cognitive dissonance)
 Only 8 interventions targeted PV
• Used 1st strategy
 Majority measured PoR, consistent with what was
targeted
A recent empirical example
 HIV disproportionately affects Blacks and
Hispanics in the U.S. (CDC, 2008)
 Incarcerated populations 5-6 times more likely to
be infected than general population
(Lopez et al., 2001)
 Social antecedents of PV/PoR?
 PV: past HIV risk behavior, past HIV testing
 PoR: believe HIV is a problem in community, know
someone who is infected
Research Qs
 Are PV and PoR empirically distinct from one another?
 Would heterosexual individuals impacted by incarceration




have higher levels of PV and PoR than non-impacted
individuals?
Is PV higher with reports of past HIV risk behavior and less
frequent HIV testing?
Is PoR higher when people believe HIV is a serious
problem in their community and/or whether they know
someone infected?
Are there different relationships between the social
antecedents of PV and PoR for each sample?
What is the relationship between PV and PoR and attitudes
towards condoms, intentions, and condom use?
Method
 Participants
 Two heterosexual couple samples
• Impacted sample
• Non-impacted sample
 Instruments
 PV: I don’t worry about HIV
 PoR: It is really unlikely that I will get HIV
 PV determinants:
• How often are you high on non-injected drugs or alcohol when you
have sex?
• How many times have you been tested for HIV?
 PoR determinants:
• How many people do you know who have or had HIV/AIDS?
• How serious is HIV in your community?
 Condom Attitudes, Intentions and Use
RQs 1 & 2
 RQ1: Are PV and PoR distinct?
 Correlations ranged from .40-.67 for all samples
 RQ2: Do impacted individuals have higher PV and
PoR? No!
 Males: reported less PV
• t(101)= -2.65, p = .009
 Males and females less PoR
• t (101) = -6.77 men
• t (101) = -5.78 women
• ps < .001
RQ3 & 5
 Does being high in drugs or alcohol during sex
influence PV?
 Did not influence PV, or PoR
 Does previous HIV testing influence PV?
 Impacted sample tested much more frequently than nonimpacted sample
 Did not influence PV, or PoR
RQ4 & 5
 Does the belief that HIV is serious problem in the
community influence PoR?
 Impacted sample saw it as a significantly more serious
problem (ps < .001)
 Not related to PoR for any sample
 Belief lowered PV for non-impacted males!
 Does knowing someone who has/had HIV
influence PoR?
 Impacted sample knew more people
 Not related to PoR for any sample
 Knowing someone lowered PV for non-impacted males
RQ6
 Condom Attitudes
 PV predicted more positive attitudes among impacted
women and more negative attitudes among non-impacted
women
 Intentions to use condoms
 PV predicted lower intentions to use among nonimpacted women
 Condom use
 PoR for non-impacted women and impacted men
associated with lower reports of condom use
Discussion of empirical example
 PV and PoR are moderately related, but distinct
 PV and PoR lower among impacted men and
women
 Past risk behaviors and testing were not related to
PV or PoR
 Other antecedents operating?
Conclusion
 PV = affect/automatic associations
 PoR= cognitive/explicit beliefs/propositions
 Different strategies and social/environmental
determinants should be used to change them
 Measurement should reflect affective and cognitive
aspects
Conclusion
 PV and PoR should operate similarly across
different negative health outcomes
 HIV, cancer, diabetes
 Considerable differences may exist between
individuals and groups of differing risks
 Once differences are identified, explore reasons
behind the differences, then develop tailored
interventions
 E.g., experimental testing of social/environmental
determinants for change among specific groups
Future directions
 Create a valid measure of PV and PoR
 In progress now
 Retest interventions that have manipulated PV
and/or PoR using new measure to determine if
change occurs
 Manipulate external/situational cues to determine
effect on PV and PoR
Thanks!
 National Institute of Health #F31-MH069079, a
Grant-in-Aid from the Society for the Psychological
Study of Social Issues, and a research grant from
division 38 of the American Psychological
Association (Health Psychology)
 Kristina Wilson & Liz Keneski
 Peter McGraw, Hannah Gould, and Heather
Patrick