Evidence map›Paper›PMID 40305144›Full record

ArticleJournal of genetic counseling2025

Collaborative coding in inductive content analysis: Why, when, and how to do it.

Free Coulston, Fiona Lynch, Danya F Vears

Abstract read
In one paragraph

Article in Journal of genetic counseling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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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.

Free CoulstonThe University of Melbourne, Parkville, Victoria, Australia.ORCID 0000-0003-0230-3593
Fiona LynchThe University of Melbourne, Parkville, Victoria, Australia.ORCID 0000-0002-6386-6739
Danya F VearsThe University of Melbourne, Parkville, Victoria, Australia.ORCID 0000-0002-6290-545X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inductive content analysis (ICA) is a useful method for analyzing qualitative data in genetic counseling research. It is particularly relevant when the goal is to examine and improve practices or develop recommendations. Although ICA can be undertaken by a single analyst, ideally there is involvement of multiple analysts (or co-coders). Co-coding can bring many benefits to qualitative analysis that sits within a constructivist paradigm, including developing a representation of the data that is not only understandable to more than one individual but also richer and more nuanced. It also provides an opportunity for mentoring more junior researchers and can be an efficient way to analyze large datasets. However, co-coding requires important planning and consideration, and there is currently a paucity of clear guidance. In this paper, we provide an outline of the small body of existing literature on this topic and propose six flexible step-by-step components of our approach to co-coding in ICA, based on our own work. We have utilized it to analyze reporting practices and perspectives for diagnostic genomic sequencing, informed consent for genetic testing, data sharing and storage, and genomic newborn screening, among other topics. To illustrate these components, we present some example vignettes to show how these procedures can be applied in different scenarios and with different analysts.

Indexed as

Genetic CounselingHumansco‐codingcollaborative codingcontent analysisinductive analysismethodsqualitative research

Identifiers

PMID40305144
PMCPMC12042989

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.