Evidence map›Paper›PMID 31097028›Full record

SynthesisBMC psychology2019

Interventions designed to reduce implicit prejudices and implicit stereotypes in real world contexts: a systematic review.

Chloë FitzGerald, Angela Martin, Delphine Berner, Samia Hurst

Abstract readSystematic Review
In one paragraph

Synthesis in BMC psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 2 pooled it
–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

105 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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45 more citing papers are in PubMed but not listed here.

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

4 authors.

Chloë FitzGeraldiEH2 (Institute for Ethics, History and the Humanities), Faculty of Medicine, University of Geneva, Geneva, Switzerland. cnsfitzgerald@gmail.com.
Angela MartinDepartment of Philosophy, University of Fribourg, Fribourg, Switzerland.
Delphine BerneriEH2 (Institute for Ethics, History and the Humanities), Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Samia HurstiEH2 (Institute for Ethics, History and the Humanities), Faculty of Medicine, University of Geneva, Geneva, Switzerland.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 32003B_149407
6 · The paper itself

Abstract

backgroundImplicit biases are present in the general population and among professionals in various domains, where they can lead to discrimination. Many interventions are used to reduce implicit bias. However, uncertainties remain as to their effectiveness.

methodsWe conducted a systematic review by searching ERIC, PUBMED and PSYCHINFO for peer-reviewed studies conducted on adults between May 2005 and April 2015, testing interventions designed to reduce implicit bias, with results measured using the Implicit Association Test (IAT) or sufficiently similar methods.

results30 articles were identified as eligible. Some techniques, such as engaging with others' perspective, appear unfruitful, at least in short term implicit bias reduction, while other techniques, such as exposure to counterstereotypical exemplars, are more promising. Robust data is lacking for many of these interventions.

conclusionsCaution is thus advised when it comes to programs aiming at reducing biases. This does not weaken the case for implementing widespread structural and institutional changes that are multiply justified.

Indexed as

StereotypingHumansPrejudiceImplicit biasImplicit prejudiceImplicit stereotypeInterventionsProfessional ethicsTrainingUnconscious bias

Identifiers

PMID31097028
PMCPMC6524213

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.