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Record W2045612295 · doi:10.1177/1468794111433089

Where to begin? Grappling with how to use participant interaction in focus group design

2012· article· en· W2045612295 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQualitative Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFocus groupParticipant observationPsychologyPerspective (graphical)Focus (optics)Meaning (existential)Social psychologyQualitative researchDistancingEpistemologySociologyComputer scienceArtificial intelligenceSocial sciencePsychotherapist

Abstract

fetched live from OpenAlex

Participant interaction is said to be the hallmark of the focus group method, but a number of studies suggest that the defining feature of the method is virtually absent in most focus group research. Our meta-analysis of this debate over participant interaction in the focus group literature suggests that absence of interaction data reflects a philosophical position, rather than neglect. Participant interaction is treated differently in different types of research, reflecting a tacit division between researchers who view the participants primarily as individuals sharing held truths and those who view them as social beings co-constructing meaning while in the focus group. We question the habit of making assumptions about the ‘proper’ use of participant interaction and call for further reflection on its role and usage in light of the aim of each study. We argue that the treatment of participant interaction needs to be a conscious and explicit design decision – one clearly rooted in a theoretical perspective and best suited to the research purpose. While exploring this issue, we discuss how a researcher’s lens affects how they deal with the interaction of participants, what they view as strengths and limitations of the method, and what kinds of results they end up with. We provide an overview of alternative approaches to participant interaction, offer strategies from different disciplines for analysing interaction, and propose a continuum of use demonstrating a range of options for when to use interaction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.713
GPT teacher head0.635
Teacher spread0.078 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it