ArticleBMJ open2020
Realist Evaluation of Autism ServiCe Delivery (RE-ASCeD): which diagnostic pathways work best, for whom and in what context? Protocol for a rapid realist review.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04422483 (A Realist Evaluation of Autism ServiCe Delivery), which is not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Realist Evaluation of Autism ServiCe Delivery (RE-ASCeD): Which Diagnostic Pathways Work Best, for Whom, When, and at What Cost?
Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- How can we improve the timeliness and quality of diagnostic assessment for children with possible autism? Qualitative findings and recommendations from a Realist Evaluation of Autism Service delivery in the United Kingdom.Autism : the international journal of research and practice · 2026Article
- An observational study of individual child journeys through autism diagnostic pathways, and associated costs, in the UK National Health Service.Frontiers in rehabilitation sciences · 2023Article
- Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe National Health Service (NHS) Long-Term Plan (2019) acknowledges that children and young people with suspected autism wait too long for diagnostic assessment and sets out to reduce waiting times. However, diagnostic pathways vary with limited evidence on what model works best, for whom and in what circumstances. The National Autism Plan for Children (2003) recommended that assessment should be completed within 13 weeks but referral to diagnosis can take as long as 799 days.This Rapid Realist Review (RRR) is the first work package in a national programme of research: a Realist Evaluation of Autism ServiCe Delivery (RE-ASCeD). We explore how particular approaches may deliver high-quality and timely autism diagnostic services for children with possible autism; high quality is defined as compliant with National Institute for Heath and Care Excellence (2011) guidelines, and timely as a pathway lasting no more than one calendar year, based on previous work. METHODS AND ANALYSIS: RRR is a well-established approach to synthesising evidence within a compressed timeframe to identify models of service delivery leading to desired outcomes. RRR works backwards from intended outcomes, identified by NICE guidelines and the NHS England Long-Term Plan. The focus is a clearly defined intervention (the diagnostic pathway), associated with specific outcomes (high quality and timely), within a particular set of parameters (Autism and Child & Adolescent Mental Health services in the UK). Our Expert Stakeholder Group consists of policymakers, content experts and knowledge users with a wide range of experience to supplement, tailor and expedite the process. The RRR is consistent with Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) and includes identifying the research question, searching for information, quality appraisal, data extraction, synthesising the evidence, validation of findings with experts and dissemination. ETHICS AND DISSEMINATION: Ethical approval not required. Findings will inform the wider RE-ASCeD evaluation and be reported to NHS England. TRIAL REGISTRATION NUMBER: NCT04422483. This protocol relates to Pre-results.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.