perceived vulnerability and perceived risk: Implications for health - - PowerPoint PPT Presentation

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

• f 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 Motivation Behavioral Skills Behavior

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 &

• ther automatic associative

processes

 E.g., fear-smoking

PoR

 Linkages between

acquired information and attitude object

 E.g., beliefs about exercise-

diabetes

 Probability important

PV and PoR and health outcomes

Negative Relationship?

 Defensive behavior activation

 Optimistic biases (e.g., Lek & Bishop, 1995)  Denial

Positive Relationship?

 Protective behavior activation

 E.g., PV + condom use

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

• utcomes

 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

59 studies remained after through review 936 Total Citations 90 “eligible” articles

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 non-

impacted 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 non-

impacted 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