Evidence map›Paper›PMID 38976145›Full record

ArticleMedicine, health care, and philosophy2024

Why we should not "help bad choosers:" screening, nudging, and epistemic risk.

Jay Zameska

Abstract read
In one paragraph

Article in Medicine, health care, and philosophy, 2024. 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. Review
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

1 author.

Jay ZameskaInterdisciplinary Centre for Ethics, Jagiellonian University, Grodzka 52, Krakow, 31-044, Poland. jay.zameska@uj.edu.pl.ORCID http://orcid.org/0000-0002-1682-5985

Funding

H2020 Marie Skłodowska-Curie Actions 2022/47/P/HS1/02511Narodowe Centrum Nauki 2022/47/P/HS1/02511
6 · The paper itself

Abstract

One prominent line of support for nudging in screening programs is the claim that nudging can help 'bad choosers' - that is, it can help some patients make choices more in line with their own values and preferences. In this article, I argue that due to the presence of epistemic risk in many screening programs, the argument that nudging can help 'bad choosers' should be revised or rejected. Expanding on the work of Biddle, J. B. 2020. Epistemic risks in cancer screening: Implications for ethics and policy. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 79: 101200.), I argue that epistemic risk undermines the argument that nudging can help to promote patient autonomy in the context of screening. Specifically, I argue that epistemic risk results in the inclusion of non-patient values and preferences in the screening process, which challenges the claim that nudging can help patients make choices more in line with their own values and preferences. I present four reasons to think epistemic risk undermines the argument in this way: (1) conflicting values; (2) lack of transparency; (3) limited autonomy in opting out; (4) unjustified manipulation. The presence of epistemic risk in screening programs means that nudging may not always be an effective means of promoting patient autonomy and informed consent. As such, epistemic risk poses significant challenges to at least one ethical justification of nudging in screening programs, and raises further questions about the role of nudging in promoting patient decision-making.

Indexed as

Personal AutonomyChoice BehaviorDecision MakingEarly Detection of CancerHumansPatient PreferencePhilosophy, MedicalEpistemic riskEthics of screeningHealthcare ethicsNudgingPatient decision-making

Identifiers

PMID38976145
PMCPMC11310277

What OpenQuestion holds

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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.