Evidence map›Paper›PMID 38816441›Full record

ArticlePediatric research2024

The Ladder of Inference as a tool to reduce implicit bias in pediatric clinical practice.

Beatrice E Lechner, Stephanie K Kukora, Autumn Fiester

Abstract read
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In one paragraph

Article in Pediatric research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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.

Beatrice E LechnerDivision of Neonatology, Women and Infants Hospital of Rhode Island, Providence, RI, USA. blechner@wihri.org.ORCID 0000-0002-3730-9082
Stephanie K KukoraDivision of Neonatology and Bioethics Center, Children's Mercy Hospital, Kansas City, MO, USA.
Autumn FiesterDepartment of Medical Ethics and Health Policy, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implicit bias in healthcare professionals is a widespread phenomenon that leads to worse healthcare outcomes for marginalized patient populations. One tool that can help providers identify when biases are impacting the clinical care they are providing and enable them to take corrective action in real time is the "Ladder of Inference" (LOI). The LOI is an instrument that elucidates the process by which we take in information about another person, filter that data through our own interests, needs, perspectives and biases, and then use it to draw conclusions about the individual. These conclusions are often profoundly inaccurate, yet we then act upon them. Thus, we propose the LOI as an "implicit bias detection tool" for neonatal intensive care unit (NICU) clinical practice. We demonstrate on two common NICU cases how utilizing the LOI can uncover mechanisms by which positive and negative feedback loops secondary to unregulated implicit bias lead to a stepwise increase or decrease in the quality of care. As the cases demonstrate, the subtle differences in individual steps up the ladder can lead to care differences of a large magnitude in either direction, hugely positive or detrimentally negative. This shift in the quality of care, then, may contribute to the significant neonatal outcome disparities in infants from minoritized groups. Using the LOI as a practical tool, we demonstrate how it becomes possible to detect one's own implicit biases and thus to consciously monitor the inferences we are making about patients and their families in order to counteract them.

Indexed as

Attitude of Health PersonnelHealth PersonnelPediatricsPrejudiceBiasHumansInfant, NewbornIntensive Care Units, NeonatalQuality of Health Care

Identifiers

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Registered trials

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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.