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London Dataset Project

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3:00 Exercise 1: Prioritising PIs. 3:30 Break ... To enable LSCBs to benchmark against statistical neighbours more effectively. Benefits ... – PowerPoint PPT presentation

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Title: London Dataset Project


1
London Dataset Project
  • Barry Hope Nick Scott
  • LB of Waltham Forest

2
Purpose of this Seminar
  • Introduce the Project
  • Present Progress to date
  • Further work on PIs and outcome measures
  • Answer questions

3
Process for today
  • 200 -Introduction to the project
  • Progress Report
  • Questions
  • 300 Exercise 1 Prioritising PIs
  • 330 Break
  • 345 Exercise 2 Identifying assumptions and
    barriers
  • 415 Plenary and Discussion
  • 500 Close

4
Purpose of the Project
  • To agree on a minimum dataset that can be
    collected by and for LSCBs across London
  • To provide an overview of safeguarding outcomes
  • To enable LSCBs to benchmark against statistical
    neighbours more effectively

5
Benefits
  • Better quality data at local and pan London level
  • Makes the best use of data already collected
    across London
  • Enables pan-London issues and trends to be
    identified
  • Allows local issues to be seen in the context of
    the broader picture

6
Barriers
  • Some changes locally to the way data is currently
    collected and reported on
  • Not all LSCBs are at the same point in terms of
    scope of interest/focus
  • Not all data will be available in the same
    timescales

7
Project plan
  • Mapping exercise what data are LSCBs already
    collecting aim was to identify what was in
    common
  • Conceptual model  aim was to develop a set of
    outcome measures
  • Pilot aim is to test feasibility

8
Mapping exercise
Large dataset but focused on Child Protection
Large unfocuseddataset
Size
Streamlined dataset with broader safeguarding focu
s
Small dataset focused on Child Protection or
local issues
Scope
9
Mapping exercise findings
  • Wide variety
  • Combination of hard and soft data
  • Some data broadly related to outcomes, some
    related to LSCB processes, some to Section 11
    standards
  • Need for scoping

10
Scoping the dataset
Section 11 compliance
Outcomes for Children
LSCB processes and standards
Contextual data
11
Conceptual model
  • Need for structured approach to setting outcome
    measures
  • Need to understand the underlying assumptions
  • Need to define the domains of the model

12
Logical Framework
Context -environment, resources, local priorities
Activity - what LSCB members do
LSCB
Outputs - measurable unitsof work completed
Outcomes - what is achieved for children
13
Outcome areas
  • Children are protected from harm
  • The health of children and young people is
    safeguarded
  • The environment is safe for children and young
    people
  • The welfare of children and young people is
    improved

14
Outcomes - notes
  • These are a subset of the Stay Safe outcome,
    NOT the ECM outcomes
  • They are broader than child protection
  • There are - inevitably - overlaps between them

15
Detailed outcomes
  • An iterative process
  • At least three different configurations
  • Consulted widely
  • Acknowledge SC concerns that CP was not visible
    enough
  • Reflect 4-tier approach to services

16
Tiers
1
2
3
4
LAC are safe from harm
child protection
e-safety education
Protection
childrens substance use
teenage pregnancy
Health
immunisation
access to child care
accidental injury
Environment
bullying harassment
welfare of LAC
suicide self harm
emotional well being
Welfare
17
Next steps
  • Fitting PIs into outcomes
  • Deciding on which belong to a later phase
  • Cluster PIs where helpful
  • Simplify

18
Refining PIs
  • Recognition that we will have to use proxy
    indicators
  • Simplicity - as few as possible
  • Validity - choose the right ones
  • Feasibility - Are they readily available?
  • Triangulation - cluster different PIs to give a
    more valid result

19
Pilot sites
20
Exercise
  • Each table will look at a different set of
    outcomes
  • Aim is to arrive at a simplified set of PIs under
    each outcome
  • This will feed into the next step of the project

21
Criteria
  • Simplicity as few as possible
  • Validity the PI gives us the right information
  • Feasibility Are these data readily available
    (locally or pan-London)
  • Clustering would clustering Pis give us a
    better result?

22
Process
  • Stage 1 (30 mins)
  • Scan the PIs under each heading
  • Reach a decision on whether or not the PI needs
    to be included using the criteria
  • Add PIs to sheets

23
Process
  • Stage 2 (30 minutes)
  • For each heading, discuss the assumptions you are
    making in including the PIs you have
  • Please record brief notes on these
  • Identify potential barriers
  • Identify missing data
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