Evidence map›Paper›PMID 41301393›Full record

ReviewBehavioral sciences (Basel, Switzerland)2025

Interventions to Reduce Implicit Bias in High-Stakes Professional Judgements: A Systematic Review.

Isabela Merla, Fiona Gabbert, Adrian J Scott

Abstract readReview
In one paragraph

Review in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Isabela MerlaSchool of Mind, Body and Society, Goldsmiths, University of London, London SE14 6NW, UK.ORCID 0009-0007-6428-227X
Fiona GabbertSchool of Mind, Body and Society, Goldsmiths, University of London, London SE14 6NW, UK.ORCID 0000-0002-2727-1113
Adrian J ScottSchool of Mind, Body and Society, Goldsmiths, University of London, London SE14 6NW, UK.ORCID 0000-0002-4171-6318

Funding

Economic and Social Research Council ES/Y001834/1
6 · The paper itself

Abstract

A systematic review was conducted to examine interventions designed to reduce the influence of implicit bias on professional judgements, with the aim of identifying strategies relevant to forensic and legal contexts. These decisions are often made under time pressure, ambiguity, and limited information, increasing reliance on intuitive judgement and mental shortcuts that can allow bias to shape how information is evaluated. Eight databases were searched and screened using predefined inclusion criteria. Studies were included if they assessed the behavioural impact of a bias-reduction intervention on decisions made by professionals or mock professionals in forensic, legal, healthcare, educational, or organisational settings. Thirty-eight studies met the inclusion criteria and were analysed. Interventions were mapped by mechanism, delivery format, and decision context. Systemic strategies, such as decision protocols, standardised rubrics, or changes to how information was presented, consistently outperformed individual-level approaches focused on changing attitudes or awareness. Effective interventions typically constrained discretion or embedded structured prompts at the point of judgement. However, most were tested in simulated settings, with limited evidence of long-term or applied effects. The review identifies strategies with the strongest empirical support and highlights those most effective, practical, and transferable to forensic and legal contexts.

Indexed as

bias reductiondecision-makingevidence-based practiceimplicit biaslegal judgementprofessional judgement

Identifiers

PMID41301393
PMCPMC12649508

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.