Evidence map›Paper›PMID 40848098›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2025

Qualitative Mediation Analysis: an Important Method for Exploring Mediating Mechanisms in Prevention Science.

Joanna J Kim, David P MacKinnon

Abstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

2 authors.

Joanna J KimDepartment of Psychology, Arizona State University, Tempe, AZ, USA. JoannaKim@asu.edu.ORCID 0000-0002-3298-7757
David P MacKinnonDepartment of Psychology, Arizona State University, Tempe, AZ, USA.ORCID 0000-0003-0866-6010

Funding

Estimating Mediation Effects in Prevention StudiesR37DA009757 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI MACKINNON, DAVID P · 2015 to 2024
$3.5M
Digital implementation support strategies for caregiver home practiceK01DA055118 · NIDA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI Joanna Jandee Kim · 2022 to 2026
$880k
NIDA NIH HHS K01 DA055118NIDA NIH HHS K01DA055118NIDA NIH HHS R37 DA009757
6 · The paper itself

Abstract

Mediating variables serve a primary role in devising intervention theories and applying them to practice. Prevention scientists have repeatedly called for understanding how and why an independent variable (X; e.g., intervention) is related to a dependent variable (Y; e.g., drug use). Quantitative mediation is used to describe mediating variables that intervene in the causal path from X to Y. Most methodological development for mediation analysis has focused on statistical methods and the assumptions necessary for valid application of these statistical methods. The current paper describes how qualitative methods extend into mediation research and the unique strength of qualitative mediation in identifying potential mediators and mechanisms of change not previously hypothesized. Taking examples from prevention research, we outline how qualitative mediation generates unique and complementary information about mediating mechanisms that may only be available through interviews, focus groups, observation, archival analysis, and other qualitative methodology. We briefly describe cautions when using qualitative mediation including reliance of retrospective reports, potential to influence interviewees, and selective sampling. We underline how qualitative mediation analysis is particularly well suited for exploratory studies and extracting mechanisms of action for new or adapted interventions in prevention science.

Indexed as

Mediation AnalysisPreventive MedicineQualitative ResearchHumansMechanismsMediationMethodsQualitative analysis

Identifiers

PMID40848098
PMCPMC13221136

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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.