Real world evidence

Local public service leaders need different types of evidence to underpin their work and deliver high quality support for babies, children, young people and their families. 

These different types, known as real world evidence, can be described as follows.

Padlock: Context - Science - Experience

Context evidence

Data about the environment in which services are delivered. It blends statistical, descriptive and observational data that reflect where, how, and with whom services are operating.

This includes:

  • Local demographics and epidemiology.
  • Service & system activity.
  • Performance and reach data.
  • Workforce capacity & skills.
  • Funding, governance and partnership processes.
Key uses
  • Identifying where and for whom gaps exist in access, outcomes or provision.
  • Understanding structural inequities, for example, disproportionality in referrals, assessments or service provision by race.
  • Understanding readiness for change, resource needs, and feasibility.
  • Tailoring & targeting support to local needs and priorities.
  • Benchmarking & evaluation.
Challenges
  • Fragmentation: Local data is often siloed across agencies.
  • Quality: Data may be incomplete, outdated, or insufficient to understand disparities (e.g. by ethnicity or disability).
  • Interpretation: Insufficient analytical capacity and local insight to make sense of the data meaningfully.

Experience evidence

Data about the lived experience of children, young people and families, and the learned experience of the practitioners who support families.

This includes:

  • Qualitative research.
  • Stories and case studies.
  • User feedback and experts by experience.
  • Participatory design.
Key uses
  • Ensuring that services are safe, inclusive, culturally relevant, accessible and joined up.
  • Understanding how stereotypes, stigma, and mistrust aQect service engagement.
  • Supporting understanding of real world impact.
  • Building legitimacy, trust, and empowering communities.
Challenges
  • Tokenism: Experience evidence can be undervalued or seen as anecdotal.
  • Inconsistency: Data quality & format can vary and be difficult to aggregate at scale.
  • Power: Shift to valuing and sharing decision-making with families & communities, especially those from minoritised groups whose voices are often marginalised.

Science evidence

Data collected and analysed using robust and systematic research methods such as trials, studies and reviews.

This includes:

  • Academic research on child development.
  • Intervention evaluations.
  • Implementation methods.
  • Measurement tools
Key uses
  • Understanding child risk and protective factors.
  • Building a theory of change.
  • Selecting interventions and building local monitoring.
Challenges
  • Timeliness: Evidence may lag behind practice needs.
  • Accessibility: Research findings are often not user-friendly
    for practitioners.
  • Context mismatch: What worked in one place may not in another without adaptation.
  • Measurement bias: Can focus on interventions that are easier to quantify, excluding relational or long-term change, and may overlook systemic or structural inequities.
  • Diversity: A lack of racial diversity in research samples can result in evidence that is less applicable or even misleading for racially minoritised populations.