Evidence map›Paper›PMID 42497863›Full record

ArticleSTAR protocols2026

COVID-19 education policy often relied on older research despite available new evidence.

Basil Mahfouz, Licia Capra, Geoff Mulgan

Abstract read
In one paragraph

Article in STAR protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Basil MahfouzDepartment of Science, Technology, Engineering and Public Policy (STEaPP), UCL, London, UK. Electronic address: basil.mahfouz@ucl.ac.uk.
Licia CapraDepartment of Computer Science, UCL, London, UK.
Geoff MulganDepartment of Science, Technology, Engineering and Public Policy (STEaPP), UCL, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Policymakers are increasingly relying on scientific research, but it remains unclear whether policy decisions reflect the latest available evidence. Building on a previously developed semantic matching approach, we use pretrained language-model embeddings to identify newer studies that are substantively related to research cited in policy documents. We then examine whether these related studies were already available before the policy documents were published. Applied to COVID-19 education policy, the analysis shows that policy documents often continued to cite established pre-pandemic research even when similar post-2020 studies had already been published. This pattern suggests that policy decisions do not always keep pace with newly available research. The approach provides a scalable way to diagnose how quickly policy systems incorporate emerging evidence across domains.

Indexed as

Computer sciencesHealth SciencesSystems biology

Identifiers

PMID42497863
PMCPMC13615485

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.