Evidence map›Paper›PMID 41886214›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2026

Comparing AI-Assisted Coding and Traditional Qualitative Analysis: a Study Examining Differences in Methods and Results of AI-Assisted Coding and Traditional Content Coding Using Community Engagement Data Collected During the Development of a Municipal Food Plan.

Natalie Poulos, Hannah Price, Lauren Bell, Courtney Byrd-Williams, Sergio Torres-Peralta, Edwin Marty

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2026. 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

6 authors.

Natalie PoulosDepartment of Nutritional Sciences, University of Texas at Austin, Austin, TX, USA. Natalie.Poulos@austin.utexas.edu.ORCID http://orcid.org/0000-0002-3543-0786
Hannah PriceDepartment of Nutritional Sciences, University of Texas at Austin, Austin, TX, USA.
Lauren BellDepartment of Nutritional Sciences, University of Texas at Austin, Austin, TX, USA.
Courtney Byrd-WilliamsSchool of Public Health, UT Health Science Center Houston, Austin Campus, Austin, TX, USA.
Sergio Torres-PeraltaCity of Austin, Communications and Public Information Office, Austin, TX, USA.
Edwin MartyCity of Austin, Austin Climate Action and Resilience, Austin, TX, USA.

Funding

City of Austin, Master Interlocal Agreement UTA19-000382
6 · The paper itself

Abstract

Qualitative data poses a challenge for prevention science and public health, as it is critical to explain the context of communities, health, and behavior, yet collecting and analyzing qualitative data using traditional methods is time-intensive and requires extensive training. As artificial intelligence (AI) models have improved, there is a growing interest in using AI to code qualitative data quickly and reliably. This study compares the similarities and differences in methods and results of artificial intelligence (AI)-assisted qualitative analysis to traditional qualitative content analysis using data collected during the development of a city and county-based food plan. In total, 2820 community comments were collected across 43 community events in 27 zip codes across the region between March 2023 and January 2024. AI-assisted analysis was completed using a combination of a transcription app (Post-It

Indexed as

Artificial IntelligenceCommunity ParticipationHumansQualitative ResearchAI-assisted codingArtificial intelligenceContent analysisGPTQualitative analysis

Identifiers

PMID41886214
PMCPMC13550024

What OpenQuestion holds

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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.