Transcript Document

Building a DataDriven Culture
in Nevada
June 10, 2013
The Performance Indicators Project is a collaboration of the
California Department of Social Services and UC Berkeley,
and is supported by CDSS , and the Stuart Foundation.
Summit Content Areas
• Leading with Data: A Focus on Outcomes
–
–
–
–
Review of basic terminology
Avoiding pitfalls and data abuse
Key concepts in performance measurement
Telling the NV Story: State and National Context
• Connecting Data to Practice: Defining the
problems
• From Data to Action: Strategy Development and
Implementation
• CQI Structure and Function in NV
Data Analytics 101
BASIC TERMINOLOGY AND
FORMULAS
Basic Terminology
Descriptive Data
• Point-in-time
• Trends
• Comparisons
data source: AFCARS
Basic Terminology
Process Measures - familiar to staff, relevant at a
caseworker level, current
Outcome Measures - the “big picture” measure of
system performance, especially when looked at
longitudinally
Measures of Central Tendency
Mean: the average value for a range of data
Median: the value of the middle item when the data are arranged from
smallest to largest
Mode: the value that occurs most frequently within the data
124 44 15
7 963 127 15
9 417 1763
4  4  7  9  12  15  17  63
Mean 
 16.4 = 9.7
87
9  12
Median 
 10.5 = 9
2
Mode 4
Measures of Variability
Minimum: the smallest value within the data
Maximum: the largest value within the data
Range: the overall span of the data
4 4 7 9 12 15 17 63
Minimum  4
Maximum 63
Range  63  4  59
Disaggregation
• One of the most powerful ways to work with data…
• Disaggregation involves dismantling or separating out
groups within a population to better understand the
dynamics and plan strategies for improvement
• Useful for identifying critical issues that were previously
undetected
Aggregate Permanency Outcomes
Race/Ethnicity
Region/Circuit
Age
Placement Type
Measuring Change
•How much has this measure changed over time?
•What will our performance be next quarter if we
increase or decrease by 10%
(latest yr - baseline yr)
netchange
 100
baseline yr
10% increase = baseline x 1.1
10% decrease = baseline x .90
Data Analytics 101
COMMON DATA PITFALLS
Common Pitfalls
• Small N – impact on rates and trends
• Seasonal variation
• Faulty comparisons – failing to consider
demographic and policy differences
• Outlier impact on central tendency
• Data integrity/Data entry (over or under
emphasized)
• Missing or incomplete definitions
• Data overload: lack of focus on and connection
to key outcomes
Common Pitfalls: Seasonal variation
Period 1 to 7:
10.2% reduction
Period 1 to 2:
38.5% reduction
Period 2 to 5:
41.7% increase
Common Pitfalls:
Small n (impact on rates and trends)
100% reduction!
But…from 2 children in
care to 0 children in care
57% increase!
But from 7 to 11 children in
care
Common Pitfalls and Graph Interpretation
Guilford County: First Entries by Initial Placement Type
20%
250
15%
200
150
10%
100
5%
50
0
% of all entries
# of children
300
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
# initial place Group Home
25
25
17
23
11
5
9
4
2
1
7
Total Entries
269
215
236
253
236
194
190
197
148
156
150
% Initial place Group Home
9%
12%
7%
9%
5%
3%
5%
2%
1%
1%
5%
Data source: UNC at Chapel Hill Jordan Institute for Families website. URL: http://ssw.unc.edu/ma/
0%
Avoiding data overload and
Managing with data
One thing the modern computer age has
given everyone is data. Lots and lots of
data. There is a large leap, however,
between having data and learning from it.
W. Gregory Mankiw
Professor of Economics, Harvard
New York Times, Sunday Business Section (Sept. 5 2010) p. 5
Manage with Data
Provides us the ability to:
• Compare metrics with agency mission and
practice model
• Connect to evidence-based practice and link
processes to desired outcomes
• Strategize on what work needs to be done
• Focus on end outcomes
• Identify what needs attention
• Tell the story
Manage with Data
• Pick the right measures for the job
• Prioritize reports and measures in line with
agency values, mission, vision
• Connect process measures to outcomes/practice
model
• Move beyond compliance and “gotcha”
• Make it fun!
• Celebrate success and tell the story
• Use your data to engage the community, create
urgency for action, maintain support
Pick the Right Measures for the Job
•
•
•
•
•
Federal Accountability
Overall Performance Monitoring
Outside Auditing (i.e. consent decrees,
monitors)
Contractor or Provider Performance
Office, Supervisor, Worker Level
accountability
Compliance
Performance
Continuum
Outcomes
Prioritize Measures in line with Agency
Values
• To keep children safe and at home
• To improve a child or youth’s well-being
• To facilitate a child or youth’s move to swift
& certain permanency
Performance is Guided by Your Values
as an Agency: Missouri Key Outcomes
Key Data Reports: How are they all
connected?
CFSR and PIP
NCANDS
AFCARS
ROM
SCRT
COA
SEE Results
Connecting the Dots
Case Review
Measure: Caregiver
involvement in case
planning
Management
Report: Frequency
of Visits with
Caretakers
Process Data: Accountability
Relevant to workers and
supervisors
Case Review
Measure:
Individualized
Services
Intermediate Outcomes
Relevant to workers,
supervisors, managers
Outcome
Measure:
Timely
Reunifications
Outcomes: “So What?”
Reflect Key Priorities of
Leadership
CFSR Findings: Relationship of Well-Being
to Permanency
Substantial
achievement on
Positive
ratings on
supports . . .
• Services to children,
parents, foster parents
• Involvement of parents in
case planning
• Caseworker visits with
children
• Caseworker visits with
parents
• Timely achievement
of permanency
• Preserving children’s
connections while in
foster care
Administration for Children and Families, U.S Department of Health and Human Services, Findings From the Initial Child and
Family Services Reviews, 2001–2004. Available at http://www.acf.hhs.gov/programs/cb/cwmonitoring/results/index.htm
Factors Associated with Timely Reunification,
Guardianship, and Permanent Relative Placement
The strongest
associations
with timely
permanency
included:
Caseworker Visits with Parents
Child’s Visits with Parents and
Siblings in Foster Care
Services to Children, Parents, &
Foster Parents
Family/Child Involvement in Case
Planning
ASFA Requirements Regarding
Termination of Parental Rights
Placement Stability
Administration for Children and Families, U.S Department of Health and Human Services, Findings From the Initial Child and
Family Services Reviews, 2001–2004. Available at http://www.acf.hhs.gov/programs/cb/cwmonitoring/results/index.htm
Strongest Associations Between Visits and Other
Indicators
Both Caseworker
Visits with
Parents and
Caseworker
Visits with
Children were
strongly
associated with:
Risk of harm to
children
Needs & Services for
children, parents, foster
parents
Child and parent
involvement in case
planning
Administration for Children and Families, U.S Department of Health and Human Services, Findings From the Initial Child and
Family Services Reviews, 2001–2004. Available at http://www.acf.hhs.gov/programs/cb/cwmonitoring/results/index.htm
Other Significant Associations Between
Visits and Indicators
Caseworker Visits
with Parents and
Caseworker Visits
with Children
were also
strongly
associated with:
Services to protect children at
home
Safety Outcome 1
Safety Outcome 2
Timely permanency goals
Timely reunification
Child’s visits with parents and
siblings
Relative placements
Meeting educational needs
Meeting physical health needs
Meeting mental health needs
Administration for Children and Families, U.S Department of Health and Human Services, Findings From the Initial Child and
Family Services Reviews, 2001–2004. Available at http://www.acf.hhs.gov/programs/cb/cwmonitoring/results/index.htm
Connecting process to outcomes
Use Data to Create Urgency for Action –
Target improvements based on your own
baseline
Regional variation should generate productive
discussion about differences in:
• Service array
• Community differences in reporting and tolerance for “risk”
• Differences across partner agencies, courts, juvenile
justice, behavioral health etc…
• Demographic risk factors and “case mix”
• Case loads, turnover (staff and leadership), and training
• A variety of other policy/practice differences
Your Role as a Data Leader
Grounded in
good case
practice model
principles
Develop
presentation
skills
Understand &
demystify data
Master
qualitative &
quantitative
tools
Recognize
challenges
Celebrate good
practice
Support positive
change
Act as a local
resource
Grow as
managers &
leaders
Knowing when you’ve got it right
• No more “the data are wrong”
• Folks own data, know it, act on it
• Practice people know the data, data people know
the practice
• Field pulls data, asks for reports, initiates actions
tied to the data
• Constantly talking about data in a positive way
After a really busy day, the data manager comments:
“I think I liked it better when no one paid attention to the data”
Telling the Story: Key Child Welfare
Indicators
So What’s the Story?
Describe the issue with as much detail as possible,
variation is key to hypothesis development.
• What’s happening right now for all kids?
• Has it always been this way?
• Is it true in all places, for all ages, for all
racial/ethnic groups?
• Is this indicator correlated with any others?
• Does it look the same for all types of cases, or in
places where practice is different?
interdependence between measures…
Rate of Referrals/
Substantiated Referrals
Reentry to Care
Permanency
Through
Reunification,
Adoption, or
Guardianship
Counterbalanced
Indicators of
System
Performance
Shorter
Lengths
Of Stay
Stability
Of Care
Home-Based
Services vs.
Out-of-Home
Care
Use of Least
Restrictive
Form of Care
Maintain Positive
Attachments
To Family, Friends, and
Neighbors
the current placement system*
(highly simplified)
the foster care system
CHILD IN
a bunch of
stuff happens
CHILD OUT
*adapted from Lyle, G. L., & Barker, M.A. (1998) Patterns & Spells: New approaches to conceptualizing children’s out
of home placement experiences. Chicago: American Evaluation Association Annual Conference
Trends in Out of Home Care
Nationwide, the number of children in out of home care is declining.
In NV, both the entry rate (per 1,000 children in the population) and
the in-care rate are higher than the national average.
6000
9.0
8.0
5000
NV Trends
7.0
4000
6.0
# in care on 9/30 <18
# entries in FY
5.0
# exits in FY
4.0
NV In care rate <18
3000
2000
3.0
National In care rate <18
NV Entry rate
2.0
1000
1.0
0
0.0
FY07
Data source: AFCARS
FY08
FY09
FY10
FY11
FY12
National entry rate
100
New York
Rhode Island
Iowa
Massachusetts
Michigan
New Mexico
Nebraska
Connecticut
Florida
Alaska
Ohio
Utah
California
Indiana
Illinois
Washington
National Avg
Arkansas
Maine
Mississippi
District of Columbia
Oklahoma
New Jersey
Wisconsin
Nevada
National Standard
Kentucky
Minnesota
Louisiana
South Dakota
West Virginia
Colorado
Montana
Maryland
Arizona
Tennessee
South Carolina
Idaho
Delaware
Georgia
New Hampshire
Texas
Kansas
Missouri
Puerto Rico
Pennsylvania
North Carolina
Hawaii
Virginia
Wyoming
Vermont
Alabama
Safety – The Absence of Repeat Maltreatment
Of all children who were victims of substantiated or indicated abuse or neglect during the first 6
months of the reporting year, what percent did not experience another incident of substantiated or
indicated abuse or neglect within a 6-month period? (FY10
98
96
94
92
90
88
86
84
82
Managing with Data in Child Welfare
CHILDREN ENTERING CARE
Children Entering Care: Nevada
Key Questions: Entries
• What is the entry rate – by age/race?
• Are entries increasing/decreasing? for all
groups?
• What strategies are in place/planned to
reduce entries (and re-entries) into care?
Possible reasons for county differences in entry rates:
• Service array – preventive and in home
• Standard of evidence
• Law enforcement removals
• Demographic risk factors
• A variety of other policy/practice differences
Substantial variation year to year is also common in
counties with few removals/small populations
Managing with Data in Child Welfare
CHILDREN IN CARE
POINT IN TIME
Key Questions: Children in Care
•
•
•
•
What groups of children are in care NOW
What types of placements?
How long have they been in care?
What is needed to move them to
permanency?
Washington
Oregon
New Mexico
Alaska
Kansas
Nevada
District of Columbia
Hawaii
New Jersey
Maine
Idaho
Oklahoma
Louisiana
Indiana
Illinois
Montana
California (AFCARS)
Utah
Missouri
North Carolina
Puerto Rico
Wisconsin
Florida
Maryland
New Hampshire
National
Arizona
Mississippi
New York
Michigan
Delaware
Ohio
Virginia
Texas
Tennessee
Georgia
Massachusetts
Nebraska
Kentucky
Alabama
Arkansas
Vermont
Iowa
Pennsylvania
South Dakota
South Carolina
North Dakota
Connecticut
West Virginia
Minnesota
Rhode Island
Wyoming
Colorado
Placement Type (Ages 0-17)
Of all the children (age 0-17yrs) in care on the last day of the FY, what percent were
placed in a congregate care setting? (Group home, shelter care, or residential facility:
excludes detention, and hospitalization)
40%
35%
30%
25%
20%
15%
10%
5%
0%
12%
Kansas
Oregon
Louisiana
Tennessee
Michigan
Nebraska
Washington
Ohio
Indiana
Alaska
Utah
Iowa
Wisconsin
New York
California
New Mexico
Maryland
Pennsylvania
West Virginia
Illinois
Idaho
Virginia
Georgia
Nevada
New Jersey
District of Columbia
Hawaii
Kentucky
Delaware
Missouri
Colorado
North Carolina
Wyoming
New Hampshire
Mississippi
Connecticut
National
Maine
North Dakota
Oklahoma
Massachusetts
Alabama
Florida
Rhode Island
Arizona
Minnesota
Montana
Vermont
Texas
Arkansas
South Dakota
Puerto Rico
South Carolina
Placement Type (ages 0-10)
Of all the children (age 0-10yrs) in care on the last day of the FY, what percent were
placed in a congregate care setting? (Group home, shelter care, or residential facility:
excludes detention, and hospitalization)
10%
8%
6%
4%
2%
0%
National and State Level
OUTCOMES: EXITS AND
LENGTH OF STAY
Key Questions: Permanency Outcomes
•What proportion of children entering care
will eventually reunify?
•How does this differ by age at removal?
•What percent of children remain in care
after 3 years?
•Are there differences by age/race?
•Is this trend changing over time?
Know which view to use
data
the view matters…
January 1, 2012
July 1, 2012
Source: Aron Shlonsky, University of Toronto (formerly at CSSR)
December 31, 2012
the view matters…
Nevada:
Length of Stay in Months 2011
(children in care 5 days or more)
2011 entries
N=2103
q1
(25% exited)
Jan 1, 2011
(point-in-time)
N=4135
q2
(50% exited)
2011 exits
N=3138
insufficient time elapsed to determine this estimate
q3
(75% exited)
0
5
10
15
Months in Care
20
25
30
35
40
entries, point in time and exits views…
Nevada:
Age of Children in Foster Care, 2012
45
40
39
35
Entries
30
25
%
20
23
22
15
10
5
8
7
12-14 yrs
15-17 yrs
0
<1 yr
1-5 yrs
6-11 yrs
entries, point in time and exits views…
Nevada:
Age of Children in Foster Care, 2012
45
40
39
35
37
Entries
30
25
%
20
Point in Time
29
23
22
15
10
5
14
12
8
8
7
12-14 yrs
15-17 yrs
0
<1 yr
1-5 yrs
6-11 yrs
entries, point in time and exits views…
Nevada:
Age of Children in Foster Care, 2012
45
43
40
39
35
37
Entries
30
29
25
%
20
Point in Time
27
Exits
23
22
15
10
5
14
12 11
8
13
8
7
12-14 yrs
15-17 yrs
5
0
<1 yr
1-5 yrs
6-11 yrs
Exit Cohort View…
but what about those
that remain in care?
Proportion of Exits by Type:
Statewide
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Death of child
Transfer
Runaway
No response
Live with relatives
Guardianship
Emancipation
Adoption
2007
2008
2009
2010
2011
2012
Reunification
80
Puerto Rico
Illinois
Delaware
North Carolina
Vermont
Oklahoma
Alaska
Michigan
Arizona
New Mexico
Connecticut
Washington
Maine
Texas
Virginia
Missouri
Kansas
Nevada
Florida
Maryland
California
Oregon
National
Iowa
North Dakota
Utah
New York
Montana
South Dakota
Alabama
Pennsylvania
New Jersey
New Hampshire
District of Columbia
West Virginia
Indiana
Rhode Island
Nebraska
Louisiana
Georgia
Wisconsin
Mississippi
Massachusetts
Idaho
Tennessee
Ohio
Hawaii
Kentucky
Colorado
Wyoming
Minnesota
South Carolina
Arkansas
%
Timely Reunification (entry cohort)
Timely Reunification (FY11): Measure C1.3 Of all first entries who
remain in care at least 8 days, what % reunify within 12 months?
70
60
50
40
30
20
10
0
Nationally, there has been almost no
improvement in timely reunification
Timely Reunification (C1.3 Entry Cohort)
National Median
NV
60%
50%
40%
39%
41%
42%
39%
41%
41%
41%
41%
42%
44%
FY08
FY09
FY10
37%
37%
30%
20%
10%
0%
FY05
FY06
FY07
30
Puerto Rico
North Carolina
Virginia
Texas
Maine
Mississippi
Michigan
Louisiana
Delaware
Nevada
Oklahoma
Missouri
Kansas
Idaho
South Carolina
Alaska
Georgia
Indiana
Hawaii
Alabama
New Mexico
New Jersey
Arkansas
Maryland
Utah
Tennessee
District of Columbia
Oregon
National
Connecticut
California
Illinois
Kentucky
North Dakota
Montana
Washington
West Virginia
Nebraska
New York
Ohio
Wyoming
Vermont
Florida
Massachusetts
South Dakota
Colorado
Iowa
Arizona
Wisconsin
Rhode Island
New Hampshire
Minnesota
Pennsylvania
Re-Entry after Reunification
Re-Entry (FY11) Measure C1.4 of all the children
reunified, what % re-enter care within 12 months?
25
20
15
10
5
0
Permanency for Longer Stayers
Nationally, exits to permanency among children already in care two years or more
has been improving (C3.1)
Nat'l Median
NV
60%
50%
40%
40%
31%
33%
32%
33%
34%
30%
26%
26%
26%
FY05
FY06
FY07
28%
29%
31%
FY08
FY09
FY10
20%
10%
0%
Puerto Rico
Utah
Delaware
Connecticut
Illinois
Minnesota
California
Colorado
Alabama
Oregon
South Dakota
Maryland
Virginia
District of Columbia
Massachusetts
North Dakota
Texas
New York
South Carolina
Missouri
Rhode Island
Ohio
Vermont
Arkansas
Wisconsin
Wyoming
National
Mississippi
Kentucky
Montana
North Carolina
Florida
Iowa
New Hampshire
Maine
Oklahoma
Indiana
Kansas
Michigan
Georgia
Hawaii
Louisiana
Pennsylvania
New Jersey
Tennessee
Nebraska
Washington
New Mexico
Alaska
Idaho
Arizona
Nevada
West Virginia
Permanency for Longer Stayers
Achieving Permanency for Longer Stayers (FY11) Measure C3.1: Of all
children in care at least two years, what % achieve permanency within the
following year?
60%
50%
40%
30%
20%
10%
0%
Connecting Data to Practice:
Using the CQI Framework
Continuous Quality Improvement
(CQI)…an ongoing process of
identifying, describing, and analyzing
strengths and problems and then
testing, implementing, learning from,
and revising solutions.
CQI Relies on…
• An organizational culture that is proactive
and supports continuous learning.
• A strong foundation – the mission, vision,
and values of the agency.
• The active inclusion and participation of
staff at all levels of the agency, children,
youth, families, and stakeholders
throughout the process.
Key Principles
• Use data and information from multiple
sources, qualitative and quantitative
• Data have a purpose: Identify trends and
anomalies; find areas for improvement; tell
stories about what is happening in practice
and policy
• CQI must support staff to improve
outcomes for families
Key Principles
• If it ain’t “broke”, it can probably still be
“fixed”
• CQI goes beyond “compliance” to “quality”
• Meaningful and active engagement of staff
at all levels, children, youth, families, and
stakeholders
• CQI requires training, preparation, and
consistent ongoing support
CQI Group Exercise 2
DEMYSTIFYING THE LOGIC
MODEL
• We’ve noted that:
Observe • Children are not exiting to permanency quickly enough
• And we believe it is because:
Explain
• Case management and case consultation has not been consistent
• So we plan to:
• Improve training and supervision; ensure practice is aligned with
Strategy
policy
• Which will result in ENVISIONED OUTCOME:
Outcome • An increase in children exiting to permanency within three years
Developed by NY OCFS
…if he had one hour
to save the world
he would spend 55
minutes defining the
problem
and only 5 minutes finding
the solution.
Before jumping right into solving a problem
• Step back
• Invest time and effort
• Improve understanding
Source: http://litemind.com/problem-definition/ (accessed 6/3/11)
Where
areSTATEMENT
we now?
HYPOTHESIS
: A HIGH LEVEL CAUSE AND EFFECT STATEMENT
 Observe performance on key measures: review trends and patterns
 Establish priorities by considering: mandates, greatest areas of
need/opportunity for impact etc…
 Explain/Explore key underlying factors: both internal and external
 Consider subpopulations: is performance different by age? Race?
Maltreatment
type? that:
We have noted
So we plan
Which will
We believe
it is&because:
to:
result in:
 Define
strengths
areas needing improvement
Administrative Data is only one part of the assessment.
LOGIC MODEL: DIGGING DEEPER – MORE DETAIL
Needs and Strengths
Assessment
Activities
Outputs
Initial and
Intermediate
Key End
Outcomes
HYPOTHESIS STATEMENT: A HIGH LEVEL CAUSE AND EFFECT STATEMENT
Where do we want to be? What are the ultimate
outcomes
that we hope
to achieve? Outcome
Observe
Explain
Prescribe




Reduce entries into care
Improve likelihood and timeliness of a permanent exit
Reduce
re-entry
We have
noted that:
So we plan
We believe
it is
because:
Improve
health,
mental
health and education to:
indicators
Which will
result in:
LOGIC MODEL: DIGGING DEEPER – MORE DETAIL
Observe and
Explain
Needs and Strengths
Assessment
Strategies
Activities
Outputs
Outcome
Short Term
Outcomes
Long Term
Outcomes
Group Exercise!!
• We examined the data and noted that:
Observe
• How are you doing on key outcomes? Are they going in the
right direction? Is this true everywhere, and for all children?
What other indicators are related to this outcome?
• And we believe it is because:
Explain
• Why? Start with brainstorming, then look to a variety of
existing data – where is there variation? What more do you
need to know? How will you find out?
Observe
Developed by NY OCFS
Explain
Strategy
Outcome
Table Discussion 1 – Trends in Timely
Permanency
Review regional data packets and CQI handout:
Focus on WHAT and WHY
• Describe the trends in timely permanency and related
measures
• Are these indicators
– Increasing?
– Decreasing?
– Staying about the same?
• What does this tell us?
• What more do we need to know?
CQI Group Exercise 2
MOVING FROM DATA TO
ACTION
HYPOTHESIS
TATEMENT: A HIGH LEVEL CAUSE AND EFFECT STATEMENT
What
willSwe
do to address the issue?
Strategies should align with the strengths and needs. What activities are
supporting
good performance?
the barriers?
Observe
ExplainWhat are Prescribe
Outcome
Consider Strategies: What do you control? Where do you need to partner
or advocate?



We have noted that:
Training
We believe it is because:
Programs/Services
Policies/practices
So we plan
to:
Which will
result in:
LOGIC MODEL: DIGGING DEEPER – MORE DETAIL
Needs and Strengths
Assessment
Activities
Outputs
Short term
outcomes
Long Term
Outcomes
HYPOTHESIS STATEMENT: A HIGH LEVEL CAUSE AND EFFECT STATEMENT
How do we know that the strategy was implemented
as planned? What are our timeframes?
Examples of outputs: Often a count (and percent)




# of people trained
# ofWe
clients
haveserved
noted that:
#We
of believe
referralsit is because:
# of meetings held
So we plan
to:
Which will
result in:
LOGIC MODEL: DIGGING DEEPER – MORE DETAIL
Needs and Strengths
Assessment
Activities
Outputs
Short Term
Outcomes
Long Term
Outcomes
How
will Swe
know
theSright
HYPOTHESIS
TATEMENT
: A we
HIGHare
LEVELheading
CAUSE ANDin
EFFECT
TATEMENT
direction?
Short
Term outcomes can
be expected to Prescribe
change quickly.
Observe
Explain
Examples of measurable improvements:
 Improve diligent search and engagement
We have
that:
So we plan
 Reduce
timenoted
to adjudication
and disposition
We believe
it is permanency
because:
 Increase
timely
hearings to:
 Improved family engagement in case planning
Outcome
Which will
result in:
LOGIC MODEL: DIGGING DEEPER – MORE DETAIL
Observe and
Explain
Needs and Strengths
Assessment
Strategies
Activities
Outputs
Outcome
Short Term
Outcomes
Long Term
Outcomes