Day 5 slides - Association of Maternal & Child Health Programs
advertisement
State Example: Translating Infant Mortality Toolkit Content • This slide set, provided by Elizabeth J Conrey, PhD, RD, is an example of how the content from the Infant Mortality Toolkit can be translated for training public health practitioners • The slides are a subset from the course titled: The Epidemiology of Maternal and Infant Health for State and Local Practitioners, given at the Ohio State University Summer Program in Population Health • Examples provided are from the Ohio Department of Health State Example: Translating Infant Mortality Toolkit Content Content from Day 5 - Toolkit Chapter Addressed: • Communicating Your Findings • The slides include detail from one of the key resources cited in the toolkit chapter, Making Data Talk: Communicating Public Health Data to the Public, Policy Makers, and the Press • This slides pull content from the accompanying workbook published by the National Cancer Institute to describe the techniques you can use to select and communicate quantitative data in ways lay audiences can understand The Epidemiology of Maternal and Infant Health for State and Local Practitioners Elizabeth J Conrey, PhD, RD July 22-26, 2015 The Ohio State University, 2015 Summer Program in Population Health Communicating Public Health Data Making Data Talk Demand Skill for health information continues of public health professionals to provide =? Better data communication will chances that findings influence public health practice http://www.choiceproject.wustl.edu/ Making Data Talk OBJECTIVES Summarize selection and presentation of public health data Provide practical suggestions on how to better communicate with the public, policy makers, & the press http://www.cancer.gov/cancertopics/cancerlibrary/MDT-Workbook.pdf Making Data Talk How do you summarize and convey data so they make sense to someone who may not be familiar with the topic, let alone the basics of epidemiology or statistics? How do you package and present data to answer the question often asked by busy people with competing demands and time constraints: why should I care? You CAN Make Data Talk and Be Understood Sharing information with the public is a public health RESPONSIBILITY Communication COMPLEX Series of choices: How to convey what you know / or findings or analyses so that audience can understand AND make decisions for programs / practices / policies? Plain Writing & Clear Communication Plain Writing Act of 2010 Requires federal agencies to write plainly when communicate with public www.plainlanguage.gov Health Literacy Ability to get, process, & understand basic health info/svcs to make sound health decisions www.cdc.gov/healthliteracy Clear Communication Index To assess and develop communication products for general public Use if you write/edit/design/review www.cdc.gov/ccindex/index OPT-In Framework Helps you organize communication process Organize Plan Test Integrate Use what you know about your audience 3 Important Audiences to public health General Policy Public Makers Press Each has expertise in something Each outsider to culture of the scientific community Contrasts between Scientists and Lay Audiences SCIENTISTS LAY AUDIENCES Sources and definition of acceptable evidence Narrow Broad Belief in rational decision making Strong Variable Acceptance of uncertainty High Low Level of interest in scientific topic High Medium to Low Quantitative and science literacy High Low Ability and interest to review extensive amounts of data High Low Expectations for receiving scientific data Why should I believe or do what you recommend? How did you reach your conclusion (rationale)? [I already have a belief about this and I’m not going to change my mind just because you tell me to] What do I do with this information? What action should I take? ETHICS Many lay audiences trust scientists/experts Responsibility to maintain that trust Careful not to mislead or omit Lead people to conclusions based on sound data that are wellreasoned and well-presented Tips for Presenting Audience-Friendly Data TIP • Avoid terms not frequently used of the scientific community EXAMPLE / EXPLANATION Cohort, Longitudinal • Avoid terms with multiple meanings Surveillance • Avoid terms with multiple meanings Proportions, relative risks • Avoid science and math concepts that can be MISUNDERSTOOD. If MUST use, explain first When making decisions, many people use heuristics (short cuts) rather than rational decision-making model • Explain how the data may impact the audience Demonstrating impact can help audience understand why data are relevant • Present data in distinctive way that helps gain audience attention For majority in US, health issues moderate/low interest. Presenting relevant / interesting will reduce “tuning out” Use Communication Fundamentals to Your Advantage Basic communication elements/model Key Audiences Storyline (how messages can support one) Basic Communication Model Basic Communication Model CONSIDER: Purpose, Strategy, & Context Purpose: why is the message being communicated? Four most common in public health Increase knowledge Instruct Facilitate informed decision-making Persuade Which applies to the message you are sending?? Strategy: what is the approach to gain attention? ACTIVE! Media campaign Encourage word of mouth Town halls PASSIVE Post information to be found by those who are seeking PUSH-PULL Uses both CONTEXT: What factors influence receipt / interpretation? Often outside sender’s control Influence at multiple points Can include Other sources of information (i.e., Jenny McCarthy) Personal experience (I put my first child to sleep on their stomach) Cultural beliefs, values, misperceptions (We take care of ourselves) Competing priorities (Food) MESSAGES Support a STORYLINE STORYLINE – major conclusions that scientists / health practitioners want audience to understand. Bottom line. Each message “Chunk” of information Based on scientific knowledge and understanding Stands alone, BUT Collectively provides rationale for storyline (main theme) MESSAGES Consider if your story is supported by “Settled Limited Science” PURSUASIVE / INSTRUCTIVE supporting knowledge or no consensus INFORM DECISION-MAKING PROCESS SOURCES: Types TYPES DESCRIPTIONS EXAMPLE Interpersonal sources People who share information through 1:1 interaction Family members, friends, colleagues, health care providers Mediated sources People who share information through 1:many interactions Journalists, politicians CHANNELS: Types TYPES DESCRIPTIONS EXAMPLE Interpersonal sources Ways of sharing information that involve personal contact Phone conversations, oral presentations, personal emails, doctor visits, text messages, social media/networking Mediated sources Ways of sharing information that are impersonal and typically reach large numbers of people at a time Newspapers, mailers, newsletters, websites, TV, billboards, radio CHANNELS Consider Availability (access??) Preference (how obtain information?) Credibility (believable, trustworthy?) Change frequently ! Consult latest research to understand habits/behaviors of audience AUDIENCES General public Policy Makers (including administrators in your own agency!) Authority to make decisions that affect public health Press COMPARING LAY AUDIENCES Individual characteristics Occupational and institutional factors Regular sources of information Help Lay Audiences Understand Your Data Audience tendencies (can influence perception) Biases Techniques to overcome tendencies and biases Be Aware of Audience Tendencies Rarely prepared to process messages containing qualitative data Quantitative literacy varies Probability estimates (1 in 200 vs. 1 in 25) Percentages Converting proportions into percentages SIMPLIFY Messages, or Provide additional explanation, or MODIFY approach Common Mistakes when Interpreting Numbers Misunderstanding probability estimates Which risk is greater? 1 in 200 or 1 in 20 Misunderstanding Percentages Improperly converting proportions to percentages To overcome quantitative literacy differences a. simplify message b. provide additional information, or c. modify approach to increase audience understanding Information Processing: General Factors Cognitive Processing Limits Limited capacity to process large amounts of information at once So people “chunk” (e.g., phone numbers) Information Processing: General Factors Satisficing We limit mental energy spent on obtaining information Stop when its “good enough” for purposes Information Processing: General Factors Expectations of Experts and Challenge of Uncertainty Lay audiences want experts with experience & credentials to provide definitive, prescriptive information Is it your alternator? Or 30% chance that it’s the alternator? Information Processing: General Factors Processing Risk Information Misunderstanding of concepts related to risk Absolute risk Lifetime risk Cumulative risk e.g., repetition of low risk behavior increases cumulative risk over lifetime Information Processing: General Factors Framing Consistency with common public frames or models? Loss Frame: Possibility of colon cancer over minor discomforts of colonoscopy Gain Frame: Associate rewards (losing weight, looking fit) with exercise Information Processing: General Factors Scanning Quick scan of written or visual material to decide if it interests them Draw conclusions about major points Try to identify the bottom line Information Processing: General Factors Use of Contextual Clues People look for clues to help process & understand new info Especially when complex, detailed, or new format Information Processing: General Factors Resistance to Persuasion Natural resistance to persuasion Often engage in “defensive processing” Approach that blunts messages inconsistent with current behavior Information Processing: General Factors Role of Emotion Motivating influence on behavior Heighten arousal Orient attention Prompt self-reflection Be Aware of Audience Biases Representativeness heuristic Anchoring and adjustment bias Correlation = causation Failure to consider randomness Strategies to overcome tendencies and biases Determine whether data should be presented Be brief and concise Be complete and transparent in portraying statistics Identify and counter mistaken health-related audience beliefs Use familiar types of data and explain key scientific or mathematical concepts Address uncertainty directly Ensure usability Provide contextual information A university research department decides not to release findings from a phase I clinical trial because of concern that the promise of a pharmaceutical treatment showing that 80% of participants had complete resolution of their disease symptoms may create great excitement that will be followed by disappointing results in phase II. This decision shows a consideration for which of the following? a. Resistance to persuasion b. Anchoring and adjustment bias c. Failure to consider randomness d. Satisficing To help explain a new report that conveys the latest statistics related to breast cancer incidence, communicators develop a graphic that compares this year’s figures to figures from the previous 5 years. This graphic helps address the following: a. Processing of risk information b. Role of emotion c. Use of contextual clues d. Satisficing During a media interview, a study’s lead scientist answers a question related to the brain’s role in the development of addiction. After the reporter takes notes, the scientist reiterates that a particular brain area doesn’t cause addiction, but that it plays a role in the development of addiction. This shows the scientists attempt to overcome which of the following? a. Information framing effects b. Processing of risk information c. Failure to consider randomness d. Correlation equals causation A doctor conducts an interview to discuss health conditions affecting women. During the interview, the doctor acknowledges that many women perceive breast cancer to be the primary killer of women. He provides statistics showing that heart disease kills more women than breast cancer and then reiterates that women should be just as aware of heart disease as breast cancer. This technique helps overcome the following: a. Resistance to persuasion b. Scanning c. Failure to consider randomness d. Anchoring and adjustment bias Presenting Data Effectively Think more about what you want your audience to understand and less about what you want to say Use communication tools to help people build knowledge Do it accurately and ethically Perception is Everything People consider items close together in a visual field to be related Can you use this to promote understanding Eyes follow lines and directions implied by separate elements within visual field Line graph vs. bar graph? subheadings People “fill in” information to help them make sense Avoid allowing to fill in with wrong information Test messages Communicate with various methods Text labels Verbal qualifiers Metaphors Narratives Tips for communicating with numbers TO INSTRUCT AND INFORM Keep numbers simple (round to nearest whole #) Use modifiers to add meaning Online use numbers instead of words (2 vs. two) Pretest Tips for communicating with numbers TO PERSUADE OR MOTIVATE Limit to 3 or fewer numbers Use familiar units Avoid unnecessary precision (round) Especially large numbers can be persuasive Tips for communicating with numbers IN TABLES Used to make comparisons or search Provide organizational cues (bolded, shaded, etc) Draw attention to significant findings Be consistent (decimal placement, headers) Visual Symbols Basics Visual Symbols Basics Visual Symbols Basics Visual Symbols Basics Visual Symbols Basics Visual Symbols Basics Question: How can the bar chart be modified to make it more effective? Question: Is there a better way to present the data found in this line graph? Explain. Question: Why was a map a good visual symbol to use for this data presentation? Question: How would you modify this pie chart to make it more effective and/or easier to read or use?
