Group Concept Mapping for Program Planning, Action ......“Group Concept Mapping is uniquely distinguished from processes generally labeled concept mapping, by virtue of two key attributes:

© 2013 Concept Systems, Inc. 1

Group Concept Mapping for

Program Planning, Action Management and Evaluation

September 25, 2013 Prepared for the Centers for Disease Control and Prevention

Concept Systems, Inc. Ithaca, NY

© 2013 Concept Systems, Inc.

Today’s Talk

What is Group Concept Mapping?

How do we do it?

What do the results of a group concept map look like?

What are some useful examples in planning and evaluation?

How is the analysis conducted?

What are some other ways that Group Concept Mapping can support an organization’s efficient and effective planning and evaluation?

What other research tools can we link to GCM to get even richer results and utility?

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Today’s Talk

We hope that there are questions and comments!

We will stop periodically and ask.

You may also enter your questions from your screen; we will answer each time we stop.

We look forward to final questions and discussion at the end.

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Thank you for inviting me

• President of Concept Systems, Inc., a woman-owned planning, research, and evaluation firm based in Ithaca, NY

• Specializing in co-authored (group) design for program planning, implementation and evaluation

• Some clients:

CDC, NACDD

NIH: OBSSR, NCI, NIAID

DHHS: OWH, SAMHSA, CMS

• Philosophy: supporting those who work to improve current conditions that affect our nation’s wellbeing, by developing and applying rigorous, reliable and relevant research tools and results

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Today’s Talk


How do we do it?






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Group Concept Mapping: A Definition

“Concept Mapping is a methodology that creates a stakeholder-authored visual geography of ideas from many communities of interest, combined with specific analysis and data interpretation methods, to produce maps that can then be used to guide planning and evaluation efforts on the issues that matter to the group.” Kane & Trochim, 2007.

“Group Concept Mapping is uniquely distinguished from processes generally labeled concept mapping, by virtue of two key attributes: active engagement of individuals and emergent rather than hierarchical structure.” Kane, 2013

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Group Concept Mapping as Participatory Inquiry*

• Actively seeks and captures the opinions, knowledge and articulations of need of “community” members

• Simple rules and processes encourage knowledge contribution at each step

• Visual outputs provide simple high-level representations of the community’s co-authored framework

• Leadership, partners and community can align to agree on implementation and evaluation priorities; a purposeful partnership.

* For example: Risisky et al (2008): Concept Mapping as a Tool to Engage a Community in Health

Disparity Identification. Ethnicity & Disease.

Anderson, LA et al (2006): Using concept mapping to develop a logic model for the Prevention Research Centers Program. Preventing Chronic Disease.

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Group Concept Mapping as a Systems Inquiry*

• Inductive and generative; allows shared meaning to emerge

• Simple rules and clear analyses generate complex patterns or results

• Visual outputs show interpretable and defensible representations of evolving thinking

• Products support autonomous agents aligning action with broader organizational or systems objectives; a default partnership.

* For example: NCI Tobacco Control MS 18 (2007): Greater Than the Sum: Systems Thinking in

Tobacco Control.

Kagan et al (2009) Developing a conceptual framework for an evaluation system for the NIAID HIV/AIDS clinical trials networks. Health Research Policy and Systems .

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Process to Output

• Group concept mapping – integrates and describes multiple perspectives at

different levels

– articulates emerging constructs from the details captured from individuals

– supports acceptance and use of the resulting framework

Knowledgeable Individual Specific Idea

Many Individuals Emerging Constructs

Group or Community Conceptual Framework

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Today’s Talk


How do we do it?






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Simple Rules for Conducting Group Concept Mapping

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Planning: Planners and key issue advisors develop a focus prompt and identify participants

Idea Generation: Communities of interest and expertise are identified, and respond with brainstormed ideas

Structuring: Communities of interest and expertise sort and rate the results of the idea development, authoring the structure and value domain of the issue

Analysis: Construct databases, produce results and compare analysis results options.

Interpretation and Use: Strategies and tactics for action follow directly from the interpretation of the results. Pattern matches and go zones help build consensus on action.

Representation: Compute the maps, pattern matches and “go zones,” and prepare them for interpretation by communities of interest.


Example:

Developing a Conceptual Framework for

an Evaluation System for the NIAID HIV/AIDS Clinical Trial Networks

Kagan et al., 2009


Some Background

• Restructuring networks

• Recompeting areas of research

• Integrating critical cross network efforts

• Clinical sites together

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Define the Issue To Be Addressed

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Work cited here is published in Kagan et al., 2009.

Specific Aims:

• Identify the goals of a large, complex research initiative across a wide array of stakeholders

• Develop a comprehensive conceptual model to guide the evaluation of outcomes of collaborative work

Planning

Focus Prompt:

“Coordinated clinical research networks will be successful if. . .”


Identify the Key Informants

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•DAIDS staff •Network investigators and leadership •the HIV/AIDS Office of Network Coordination •network evaluation coordinators •community advisory board representatives •other government agency staff with a role in the networks

Planning


Elicit Knowledge and Opinion

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Idea Generation Standardize key laboratory procedures across the networks. (1)

There is integrated use of domestic and international sites. (21)

There are standardized tool kits for use in behavioral studies. (26)

Ethics, community and behavioral teams provide input early in protocol development. (77)

Clinical trial sites successfully meet recruitment and retention goals. (84)

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Organize Knowledge & Opinion

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Rate the Ideas • Importance

Sort the Ideas

Structuring


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Analysis and Representation


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Interpretation & Use

Turn Data into Meaning


Building the Concept Map

• The Raw Materials: – Statements

– Sort Input from each participant

• The Tools – Aggregation of Sort Data

– Similarity Matrix

– Multidimensional Scaling

– Cluster analysis

– Anchoring/Bridging Analysis

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This point map map shows all the elements in relation to one another.

Point Map

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Standardized systems of accountability are integrated across networks and DAIDS. (43)

Ethics, community and behavioral teams provide input early in protocol development. (77)

“Coordinated clinical research networks will be successful if...”

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Substudies add value to the experimental design of the parent protocols (73)

Networks provide high quality, scientifically valid results (82)

Networks assess research issues and questions in the context of prevention and treatment policies. (37)

Similar Ideas Are Closer; Less Related Content is More Distant


…so that many concepts can be considered in a shared structure

Ideas are Organized into Groups

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…contains all the details and provides a conceptual framework.

The Emergent Structure

DAIDS Policies and Procedures

Management

Resource Management

Community Relations

Communication, Collaboration, Harmonization

Scientific Agenda-setting

Biomedical Objectives

Relevant to Participants


Cluster: Scientific Agenda-Setting

networks obtain scientific input and involve a large group of clinical investigators in the research agenda. (4)

networks develop protocols with attainable goals. (7)

there is collaboration with experts outside the networks. (11)

there is communication and cooperation between stakeholders in the planning of science. (16)

there is acknowledgement of and support for the scientific contributions of international research partners. (19)

networks integrate biomedical and technological advances with behavioral intervention strategies. (57)

networks focus on complementary pieces of the research agenda. (62)

networks reassess and reprioritize their scientific priorities as the field evolves. (63)

networks focus on high priority trials that will not be done in the private sector. (64)

the proposed scientific priorities and research plan is feasible. (78)



appropriate and relevant community representation is included at all levels; institute, network, and site. (31)

consideration is given to the differences in conditions in resource poor nations. (39)

the dignity and human rights of participants are respected. (42)

sites have the scientific and technical skills needed to pursue the research agenda. (52)

the community in included in every stage of a protocol (68)

research is conducted acknowledging the culture, norms and values of the community they are working with. (74)

ethics, community and behavioral teams provide input early in protocol development. (77)

research sites provide hours to make participation accessible to subjects. (80)

community support, training and education are provided. (83)

clinical trial sites successfully meet recruitment and retention goals (84)

Cluster: Community Engagement


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DAIDS has methods for managing complex endeavors. (8)

the standard operating procedures across each of the networks are consistent. (10)

there is a performance-oriented network culture. (14)

DAIDS medical officers/ program officers provide a consistent level of input/oversight to all networks. (18)

there is a streamlined protocol development and implementation process. (24)

there are uniform standards for site development. (34)

each component of the network (committees, SDMC, labs, ops centers, sites) has the authority to perform its duties and responsibilities without the interference of the others. (35)

a central IRB system is established. (38)

efficient and reasonable regulatory processing can be established. (40)

standardized systems of accountability are integrated across networks and DAIDS. (43)

protocols are closed out, analyzed and published in a timely manner. (48)

clear direction is given on which DAIDS priorities are within the scope of the networks versus which DAIDS priorities should be achieved via other grant mechanisms. (66)

DAIDS staff who work with the studies have a voice in decisions to approve proposals/ protocols. (70)

DAIDS provides clear, consistent messages about goals, objectives and expectations. (81)

there is a defined process for networks to use in reviewing future site expansion. (91)

Cluster: Operations and Management “Coordinated clinical research networks will be successful if...”

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Rating

Relative Importance 1=relatively unimportant 2=somewhat important 3=moderately important

4=very important 5=extremely important

Respondents

Self identified by •Network •Role

•Scientific Focus

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Values of All Participants


Operations and Management

Resource Utilization

Community Involvement

Collaboration, Communication, Harmonization

Scientific Agenda Setting

Biomedical Objectives Relevance to Participants

4.11

3.80


Collaboration, Communication, Harmonization





Relevance to Participants


Cluster Legend Layer Value 1 3.80 to 3.86* 2 3.86 to 3.92 3 3.92 to 3.98 4 3.98 to 4.05* 5 4.05 to 4.11*

*significantly different from * at .05 level

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Importance Ratings by Area of Science All Participants

N= 308

Vaccine Research and Development

N= 53

4.35

3.64


Collaboration, Communication,

Harmonization





Relevance to Participants


Translational Research and Drug Development

N= 38

Optimization of Clinical Mngmt

N= 112

Microbicides N= 17

Prevention of Mother to Child Transmission

N= 23

Prevention of HIV Infection

N= 23

r = .63 r = .92 r = .9 r = .83 r = .77 r = .78


3.39 2.68 4.67

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Group B

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Items perceeived by both groups as relatively less

important

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importance from Group B

Items perceived by both groups as relatively more important

Higher importance for Group B, Lower importance

for Group A

Importance mean for participant group A

Importance mean for participant group B

Go-Zone: Looking Within a Cluster


DAIDS develops a clear policy that facilitates generic drug use. (6)


DAIDS has methods for managing complex endeavors. (8)

DAIDS medical officers/ program officers provide a consistent level of input/oversight to all networks. (18)

DAIDS staff who work with the studies have a voice in decisions to approve proposals/ protocols. (70)


DAIDS policies reflect what is required for good science, protection of human subjects, and safety. (72)

DAIDS provides clear, consistent messages about goals, objectives and expectations. (81)

NIAID's budgeting process for the networks and the sites is transparent. (25)

NIAID considers multiple factors in funding networks and sites. (44)

DAIDS streamlines their monitoring/ auditing procedures. (88)

DAIDS Policies & Procedures: Prevention to Drug Development

3.96 3.21 4.75 2.69

4.73

Vaccine Research, Drug Development

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Turning Meaning into Action: Evaluation Logic Model

• Clusters as inputs, activities, outputs and outcomes for logic model development

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Using the Framework: Segmentation and Integration

• Clusters as inputs, activities, outputs and outcomes for logic model development and evaluation foci.

• Emergent structure as the basis for 4 domains, upon which more sophisticated evaluation projects were developed.

• At conclusion of 3 year evaluation project, concept map continues to highlight relationships among evaluation foci unforeseen by the evaluation team at the start of the project.

• Publications considered significant contributions to the field.

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Using the Framework: Publications

Kagan, J. M., Kane, M., Quinlan, K. M., Rosas, S., & Trochim, W. M. K. (2009). Developing a conceptual framework for an evaluation system for the NIAID HIV/AIDS Clinical Trials Networks. Health Research Policy and Systems, 7(12). doi:10.1186/1478-4505-7-12.

Kagan, J. M., Rosas, S., & Trochim, W. M. (2010). Integrating utilization-focused evaluation with business process modeling for clinical research improvement. Research Evaluation, 19(4), 239-250.

Rosas, S. R., Kagan, J. M., Schouten, J. T., Slack, P. A., & Trochim W. M. K. (2011). Evaluating Research and Impact: A Bibliometric Analysis of Research by the NIH/NIAID HIV/AIDS Clinical Trials Networks. PLOS ONE, 6(3): e17428.

Kagan, J. M., Rosas, S. R., Siskind, R. L., Campbell, R. D., Gondwe, D., Munroe, D., Trochim, W. M. K., and Schouten, J. T. (2012). Community-researcher partnerships at NIAID HIV/AIDS clinical trials sites: Insights for evaluation and enhancement. Progress in Community Health Partnerships: Research, Education, and Action, 6(3), 311-320.

Rosas, S. R., Schouten, J. T., Cope, M. T., & Kagan, J. M. (2013). Modeling the dissemination and uptake of clinical trials results. Research Evaluation, 22(3), 179-186.

Rosas, S. R., Kagan, J. M., Schouten, J. T., Varghese, S., Dixon, D., Marci, J. (under review). Evaluating Protocol Lifecycle Time Intervals and Participant Accrual in HIV/AIDS Clinical Research Trials. Submitted

Rosas, S. R., Cope, M. T., Villa, C., Motevalli, M., Utech, J., & Schouten, J. T. (under review). Assessing the Challenges of Multi-Scope Clinical Research Sites: An Example from NIH HIV/AIDS Clinical Trials Networks. Submitted, in review

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Today’s Talk


How do we do it?






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How We Conduct Analysis: Simple Rules

Enter

Individual Sort Data

Conduct Multidimensional Scaling

Conduct

Hierarchical Cluster Analysis

Similarity Matrix

Point Map

Cluster Map

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• Contains unique data on the relationship of each idea to each other idea—for every individual who took part in sorting

• Completed binary square similarity matrix reflects the sum of relationship of each statement to all others

Similarity Matrix

Binary, square similarity matrix: one participant’s

sorting data

Sort for one participant

Sorter 1

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Sorter 1

Sorter 3

Sorter 2

Similarity Matrix

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Start with total square similarity matrix: data from five participants

Sorter 1 Sorter 2 Sorter 3 Sorter 4 Sorter 5

To simplify the example, we’ll look at items 1-4 only…

Similarity Matrix

1 2 3 4 1 5 1 2 4 2 1 5 0 0 3 2 0 5 3 4 4 0 3 5

Similarity Matrix

Start with total square similarity matrix: data from five participants


Similarity Matrix

x y 1 2.8 1.0 2 5.9 3.2 3 1.1 3.2 4 1.9 1.3

Coordinate Matrix

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4 1

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y

Convert similarity matrix to distances between points—

placing points in two dimensions, like distances

between cities

1 2 3 4 1 5 1 2 4 2 1 5 0 0 3 2 0 5 3 4 4 0 3 5

Start with total square similarity matrix

We can show these as a matrix of distances

between points 1 2 3 4 1 0.0 3.2 2.8 1.1 2 3.2 0.0 4.8 4.6 3 2.8 4.8 0.0 2.1 4 1.1 4.6 2.1 0.0

Distance Matrix

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4 Conversion of similarity to distances on the map

among all points; correspondence is

stress value


Multidimensional Scaling

•Similarity Matrix= degree of relationship

•Table of Distances= calculated using similarity

•Placement in relation to each other item, based on table of distances

•Stress: relationship between similarity input and distances on the map

•Low stress = greater correspondence between similarity input and map

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• Directionality

– Does MDS know North from South?

• Dimensionality

– Why only two dimensions?

• Stress

– What does it indicate re strength of analysis?

– Benchmarking guidance

• Rosas, S and Kane, M. (2012)Quality and rigor of the concept mapping methodology: A pooled study analysis. Evaluation and Program Planning, 35(2).

Multidimensional Scaling

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• Hierarchical

– clusters get built in a tree-like method

• Agglomerative

– builds toward all items in one pile (vs. divisive - all start in one and divide)

• Clustering criterion

– Ward’s algorithm to dictate merger procedure

Cluster Analysis

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Cluster Analysis


Rosas, S. R. & Kane, M

2012

Quality and rigor of the concept mapping methodology: A pooled study analysis. Evaluation Program Planning 35(2)

236-245

http://www.ncbi.nlm.nih.gov/pumed/22221889

Under "bench" and under title (in TWICE)


1 + 6 5 + 7 9 + 10 (1 + 6) + 8 3 + 4 2 + (9 + 10) ((1 + 6) + 8)) + (3 + 4) (5 + 7) + ((2 + (9 + 10)) (((1 + 6) + 8)) + (3 + 4)) + (5 + 7) + ((2 + (9 + 10))

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Cluster Analysis


Additional Analyses

• Anchoring/Bridging:

– Lower bridging items are considered anchors; help clarify meaning of a specific area of map

– Higher bridging values connects, or bridges areas on map

• Spanning Analysis: visualization of anchoring/bridging

• Cluster Label:

– not content analysis—quantitative analysis of best “fitting” suggested cluster names from participants

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Bridging value = .06 (low)

An Anchor

Advertise the organization’s image rather than just specific programs (1)

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A Bridge

Expand the number of program facilities by 25% in next five years (49)

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Integrated Analysis Methodology

• SPSS – Enter sort data – Compute similarity matrix – Run MDS – Run Cluster Analysis (on

MDS coordinates) – Select number of clusters – Plot maps – Produce rating statistics – Produce rating maps – Produce pattern matches (in

Excel) – Produce bivariate plots – Post-process plots in

graphics program – Produce reports

• Concept System – Select sorts – Run analysis – Select number of clusters – Produce maps, pattern

matches and go-zones – Produce reports

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Today’s Talk


How do we do it?






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Healthy Aging Mobility Action Plan

CDC Division of Adult and Community Health

and

National Association of Chronic Disease Directors

An Example: Policy and Practice

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Defining the Issue

“One specific action that can lead to positive change in mobility for older adults in the U.S. is…”

To engage stakeholders and identify strategies that will contribute to promoting mobility in community-dwelling older adults as a coordinated response across agencies and organizations.

•N>200: •Architecture and Engineering •Aging •City Planning , and Urban Planning •Community Development •Disability programs/research •Family and Community Health •Geriatric Medicine •Gerontology •Public Health •Social Work and Psychology •Rehabilitation, Physical , and Occupational Therapy •Transportation


Eliciting Knowledge and Opinion

•Online brainstorming session: April 23, - May 11, 2012

(174 website visits)

302 statements

generated

6. focus research on a comprehensive definition of optimal mobility and link to outcomes such as quality of life.

20. identify one or more communities to serve as environmental models or as examples of mobility-sensitive or friendly communities.

69. promote training to help older adults and people with disabilities to overcome community barriers to participation.

80. identify and evaluate state-wide standards for hazard-free, safe and accessible public areas.

90. provide coverage for Community Health Workers to provide support and transportation.

94. support the placement of food access points (like corner stores or mobile farmer's markets) and connect locations to walking and wheeling trails, to support destination walking and riding.

102 final


Content and Structuring

Rate the Ideas •Potential Impact •Feasibility

Sort 102 Ideas

302 statements

generated 102 final


CDC HAN Mobility Point Map

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CDC HAN Mobility Concept Map

Housing andAccessibility

Policy

CoordinatedAction

Community Supports

Training

Research to Practice

Transportation

Built Environmentand Safety

Independence andEngagement

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CDC HAN Mobility Policy Go-Zone State and Local Perspective

2 7

90

101

14

29 30

43

16 61

24

96 100

Policy

3.41

2.36

1.59

Achievability

1.91 2.88 3.38

Potential Impact

r = 0.19

14. include requirements to ensure that publically-funded agencies responsible for providing transportation also participate in the local mobility planning process.

29. require coordination and integration among local, county, regional, and state entities responsible for pedestrian, cycling, and transit to ensure planning and use of best practices.

30. create state-specific guidance for Complete Streets to encourage planning and transportation funding reallocation for a range of transportation modes.

43. form coalitions of community groups with mutual interests, to develop and help adopt complete street policies that include road, sidewalk, lighting and shade polices and other supports to encourage walking while providing a safe and healthy area. 59


CDC HAN Mobility Policy Go-Zone State and Local Perspective

9

54

94

12

35

13 22

27 81

25

56 63

Built Environment and Safety

3.41

2.63

1.59

Achievability

1.91 2.91 3.38

Potential Impact

r = 0.39

25. implement strategies known to make street crossings safer for crossing assistance.

56. implement complete streets, traffic calming, and continuous sidewalks to promote safer, more functional and more aesthetically pleasing walking and wheeling environments.

63. increase use of pedestrian-friendly walkways in all areas with expanded use of crosswalks and extended traffic lights to assure safety.

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Targeting Priorities with Delphi

• State chronic disease directors provided input on priority actions for states

• Target: 5-6 highest potential actions for implementation

• Multiple iterations to determine consensus

• Process began with 20 high potential impact/ high achievability items from concept map

• Two forms of prioritization: ratings and ranking – Round 1: 20 actions rated by 39/43 respondents

– Round 2: 15 remaining actions rated by 35/43 respondents

– Round 3: 7 remaining actions ranked by 40/43 respondents

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Final Delphi Results

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

70 29 56 11 30 4 63 43 14 85 72 20 25 34 32 15 93 91 17 42

Perc

en

t

Item number

Item Agreement

Round 1 Round 2 Round 3

• 4 top priorities based on level of agreement across rounds and methods – 70: Support and implement training for city

planning and public health government staff, on model legislation, projects and programs to enact and maintain Complete Streets plans

– 56: Implement complete streets, traffic calming, and continuous sidewalks to promote safer, more functional and more aesthetically pleasing walking and wheeling environments

– 29: Require coordination and integration among local, county, regional, and state entities responsible for pedestrian, cycling, and transit to ensure planning and use of best practices

– 11: Include mobility in coordinated chronic disease prevention and health promotion state plans

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Extending GCM’s Capacity: Link other Methods

Method Broaden, Support

Framework

Plan and

Implement

Evaluate

Delphi X X

Strategy Mapping X

Logic Model X X

Survey Instruments X X

Targeted Interviews X X X

Focus Groups X X X

Priority Workshops X X

Expert Panels X X X

Assessment Plan X

Evaluation X X

Publications X X

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Some applications

•Action Planning

•Evaluation

•Needs assessment

•Organizational development

•Partnership development

•Program development

•Conceptual framework/theory development

Learning More About Group Concept Mapping

• Group concept mapping publications database – Basic methodology bibliography – AND special topic bibliographies: e.g., 112 Health, Public Health and Health Care-based

publications in 89 journals covering about 22 subjects.

Topics include • Alzheimers/brain • Cancer prevention/treatment • Community engagement • Elder care • End of life • Women’s health • Prevention research • Tobacco control

• Other publication databases available for researchers and program professionals • Kane and Trochim (2007) Concept Mapping for Planning and Evaluation. • Methodology FAQs • GCM Training Program

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Questions, Discussion

Thank you! [email protected]

Documents

Group Concept Mapping for Program Planning, Action ......“Group Concept Mapping is uniquely distinguished from processes generally labeled concept mapping, by virtue of two key attributes: