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TitleRoll Back Malaria Social and Behavior Change Communication Indicator Reference Guide
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Table of Contents
                            Acknowledgements
Contents
Acronyms
Introduction
Summary List of Priority Indicators
Part 1: Guidance on Selecting and Monitoring Indicators
	Framework for Monitoringand Evaluating Malaria SBCC Programs
	Selecting and Adapting Indicators
	Data Sources
	Gender
Part 2: Priority Indicators
	Indicator Reference Sheets
		Recall
		Knowledge
		Risk and Efficacy
		Norms
		Attitudes
		Behaviors
		Program Outputs
Part 3: Annexes
	Annex 1: Theories of Communication and Behavior Change
	Annex 2: Checklist for Reporting on Malaria SBCC Evaluations
	Annex 3: Survey Questions and Measurement Methods
	Annex 4: Case Studies for Choosing and Adapting Indicators and Questions
		Case Study 1: Selecting Indicators Based on the Program’s Stage
		Case Study 2: Adapting Indicators and Questions to Seasonal Malaria Chemoprevention
	Annex 5: References
                        
Document Text Contents
Page 1

Malaria Social and Behavior Change
Communication
Indicator Reference Guide:
Second Edition
September 2017

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Malaria Social and Behavior Change Communication Indicator Reference Guide: Second Edition

PAGE 2

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Malaria Social and Behavior Change Communication Indicator Reference Guide: Second Edition

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Numerator

Number of SBCC activities carried out
This indicator can be easily adapted to state the actual activities used.
Potential adaptations include “Number of community dialogues” and
“Number of times messages aired on radio or television in reference
period [such as three months].” Once program evaluators have
information on the number of times a message has aired, they can
triangulate this information with data from the radio and television
stations on approximate geographical coverage of their broadcasts,
and census data in order to calculate a rough estimate of how many
people were reached by the broadcasts.

Denominator

None

Measurement method

This indicator can be measured through program records that note
the number activities carried out. Managers may wish to also gather
data on characteristics of audience—such as age, sex, and location—to
provide more contextual information to this indicator.

Disaggregation

This indicator is to be disaggregated by the type of SBCC activity. The
type of activity will depend on the program design, but may include
home visits, information sessions, community demonstrations, and
television or radio airings.

Data use and interpretation

This indicator provides a measure of the implementation of a SBCC
activity, by indicating the frequency of various activities carried out.
This indicator can be used to ensure that a SBCC activity is on track
according to the activity work plans. If SBCC activities are not taking
place according to plan, then the expected behavior change is unlikely
to occur.

Strengths

• Measuring the number of SBCC activities carried out can
provide an indication as to progress of the program.

• This indicator is to be disaggregated by SBCC activity,
providing managers with detailed information about
implementation activities.

Limitations

• While the indicator measures the number of activities carried
out, it provides no information about the quality of activities.

• This indicator cannot provide information on whether the
activities occurred on time.

17. Number of people trained in SBCC for malaria

Purpose

This indicator serves as a measure of SBCC training programs.
Managers can use it to determine whether a program is meeting its
training targets and/or for tracking progress from one year to the
next. When aggregated, it also represents human resource potential of
people who could help carry out malaria SBCC activities.

Definition

This output-level indicator measures the number of people who have
completed a training course in malaria SBCC. An individual should only
be counted after they have completed the training. Individuals that
are mid-way through a training course should be counted in the next
reporting period. Individuals attending more than one peer-education
training course during a reporting period should be counted only once.

Numerator

Number of people who have completed a training course in malaria
SBCC

Denominator

None

Measurement method

Number of persons trained is based on the final list of participant
names, for potential verification of attendance and training topic. The
data sources for this indicator include training sign-in sheets, training
reports, and program reports.

Disaggregation

Data can be disaggregated by age, gender, and urban/rural residence.
If the SBCC is targeting and/or linking to inequity, classify trainees
by areas served (poor/not poor) and disaggregate the data by area
served.

Data use and interpretation

This indicator provides a measure of the available human resources
trained in malaria SBCC. The number of people trained provides an
indication of the capacity of the program to carry out the intended
SBCC activities.

Limitations

• This indicator does not capture the number of participants
who become actively involved in malaria SBCC. A further step
would be to measure the percentage of people were trained
in malaria SBCC and who are active during a reference
period.

• This indicator does not provide information on knowledge
gained or the quality of the training.

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Part 3: Annexes

Annex 1: Theories of
Communication and
Behavior Change
The indicators in this guide are based on previous research and
theories about the determinants of behavior change for malaria, family
planning, HIV, and other health areas. Data shows that improving
knowledge alone is not enough to increase the uptake of desired
behaviors. Other factors, such as audience attitudes and characteristics
of the desired behavior should also be considered.

The word “theory” is used differently in everyday speech and science.
While the vernacular use of the word “theory” implies speculation,
social science and scientific theories—like the ones discussed in this
section—instead, refer to “an explanation of some aspect of the natural
world that has been substantiated through repeated experiments.”12

Theories help us map where the audience is in the process of behavior
change and how they will get to the desired change. Theories provide
insights into the decisions, motives, barriers, and facilitators associated
with change.

In this section are six commonly used behavior change and
communication theories. While the theories share some similar
elements, each emphasizes slightly different constructs and processes.
In this annex, we provide an overview of each theory and how their
constructs are reflected in the indicator guide. This information was
adapted from the Online Training Series on Evidence-Based Malaria
Social & Behavior Change Communication13 and a series of research
primers on SBCC14.

Extended Parallel Processing Model
Indicators 6 through 9 measure the constructs of perception of risk,
self-efficacy, and response efficacy, which have been associated with
preventive behaviors.15 These constructs are based on the Extended
Parallel Processing Model (EPPM)—also known as the Risk Perception
Attitude Framework.16 The EPPM describes how reason and emotion
interact during individual decision-making.

The model has two components: fear or threat (emotion) and
efficacy (reason). Fear has two parts, severity and susceptibility, and
efficacy—or confidence in one’s ability to control or manage the threat
or risk perceived—is composed of three parts: response efficacy, self-
efficacy, and barriers.

Fear or Threat

• Susceptibility refers to the belief that the disease or threat
can actually happen to them. Indicator 7, proportion of
people who perceive they are at risk from malaria, measures
susceptibility.

• Severity refers to how serious people believe the threat
(malaria) to be. This is reflected in indicator 8, proportion of
people who feel that consequences of malaria are serious.

E�icacy

• Response efficacy refers to a perception that a proposed
action or solution will actually control the threat. In the
case of malaria, a person’s belief that ITNs serve as good
protection against malaria is an example of response
efficacy. Indicator 9, proportion of people who believe that
the recommended practice or product will reduce their risk,
measures response efficacy.

• Self-efficacy is a measure of self-confidence that a person
can perform an action to control the threat. Self-efficacy can
refer to a person’s confidence in correctly and consistently
using ITNs to prevent malaria. Indicator 10, proportion of
people who are confident in their ability to perform a specific
malaria-related behavior, measures self-efficacy.

• The last part of efficacy, barriers, refers to perceptions
of factors that may hinder someone from practicing the
behavior. Research has shown that individuals can have the
knowledge, skills, positive beliefs, attitudes, and intentions
toward a specific behavior, yet they still do not engage in
the recommended behavior. A trigger to motivate action is
needed.

Putting it all together
Evaluators can expect desirable behavioral responses when people
have strong risk/threat perceptions coupled with strong beliefs of
efficacy toward the recommended response (Figure 1, top left box).
When people experience significant fear, but have little belief that they
can take action or that their actions will effective, they will be more
likely to deny the importance of the issue, act defensively, or avoid it
(top right box). If the threat is perceived not to be serious but there
are easy and effective measures available, individuals may be slightly
motivated to act (bottom left box). If the threat is not serious and there
are no feasible or effective actions that individuals can take, they will
likely do nothing about the issue.

For example, people may feel that ITN use is easy but feel little fear
about the risk of malaria infection during the dry season (bottom left
box). SBCC activities may be designed to increase the perception that
community members remain susceptible to malaria during the dry
season and that its consequences can still be severe (top left box).
Using the indicators provided, evaluators can measure the extent to
which these programs affected perceptions of risk and efficacy, and
whether these constructs were determinants of year-round ITN use.

12 Ghose, Tia. “Just a Theory: 7 Misused Science Words,” Scientific American, April 2013. https://www.scientificamerican.com/article/just-a-theory-7-misused-science-words/
13 VectorWorks 2015.
14 Health Communication Capacity Collaborative 2014.
15 Boulay et al. 2014.
16 Rimal and Real 2008.

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• Gies S., S.O. Coulibaly, C. Ky, F.T. Ouattara, B.J. Brabin, and
U. D’Alessandro. 2009. Community-based promotional
campaign to improve uptake of intermittent preventive
antimalarial treatment in pregnancy in Burkina Faso.
American Journal of Tropical Medicine and Hygiene 80(3):
460-469.

• Hill J., J. Hoyt, A.M. van Eijk, L. D’Mello-Guyett, F.O. Ter Kuile,
R. Steketee, H. Smith, and J. Webster. 2013. Factors affecting
the delivery, access, and use of interventions to prevent
malaria in pregnancy in sub-Saharan Africa: a systematic
review and meta-analysis. PLoS Medicine 10(7): e1001488.

• Kaufman M.R., D. Rweyemamu, H. Koenker, and J. Macha.
2012. “My children and I will no longer suffer from malaria”: a
qualitative study of the acceptance and rejection of indoor
residual spraying to prevent malaria in Tanzania. Malaria
Journal 11: 220.

• Keating J., P. Hutchinson, J.M. Miller, A. Bennett, D.A.
Larsen, B. Hamainza, C. Changufu, N. Shiliya, and T.P. Eisele.
2012. A quasi-experimental evaluation of an interpersonal
communication intervention to increase insecticide-treated
net use among children in Zambia. Malaria Journal 11: 313.

• Kilian A., H. Lawford, C.N. Ujuju, T.A. Abeku, E. Nwokolo, F.
Okoh, and E. Baba. 2016. The impact of behaviour change
communication on the use of insecticide treated nets: a
secondary analysis of ten post-campaign surveys from
Nigeria. Malaria Journal 15: 422.

• Koenker H., A. Kilian, G. Hunter, A. Acosta, L. Scandurra, B.
Fagbemi, E.O. Onyefunafoa, M. Fotheringham, and M. Lynch.
2015. Impact of a behaviour change intervention on long-
lasting insecticidal net care and repair behaviour and net
condition in Nasarawa State, Nigeria. Malaria Journal 14: 18.

• Lover A.A., B.A. Sutton, A.J. Asy, and A. Wilder-Smith.
2011. An exploratory study of treated-bed nets in Timor-
Leste: patterns of intended and alternative usage. Malaria
Journal 10: 199.

• Panter-Brick C., S.E. Clarke, H. Lomas, M. Pinder, and S.W.
Lindsay. 2006. Culturally compelling strategies for behaviour
change: a social ecology model and case study in malaria
prevention. Social Science & Medicine 62(11): 2810-2825.

• Pulford J., I. Mueller, P.M. Siba, and M.W. Hetzel. 2012.
Malaria case management in Papua New Guinea prior to
the introduction of a revised treatment protocol. Malaria
Journal 11: 157.

• Russell C.L., A. Sallau, E. Emukah, P.M. Graves, G.S. Noland,
J.M. Ngondi, M. Ozaki, L. Nwankwo, E. Miri, D.A. McFarland,
F.O. Richards, and A.E. Patterson. 2015. Determinants
of bed net use in Southeast Nigeria following mass
distribution of LLINs: implications for social behavior change
interventions. PLoS ONE 10(10): e0139447.

• Scandurra L., A. Acosta, H. Koenker, D.M. Kibuuka, and S.
Harvey. 2014. “It is about how the net looks”: a qualitative
study of perceptions and practices related to mosquito net
care and repair in two districts in eastern Uganda. Malaria
Journal 13: 504.

• Strachan C.E., A. Nuwa, D. Muhangi, A.P. Okui, M.E. Helinski,
and J.K. Tibenderana. 2016. What drives the consistent use
of long-lasting insecticidal nets over time? A multi-method
qualitative study in mid-western Uganda. Malaria Journal 15:
44.

• Wijesinghe R.S., J.M. Atkinson, A. Bobogare, L. Wini, and M.
Whittaker. 2011. Exploring provider and community responses
to the new malaria diagnostic and treatment regime in
Solomon Islands. Malaria Journal 10: 3.

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