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Record W4323363531 · doi:10.1080/13645579.2023.2185985

Considerations for conducting online focus groups on sensitive topics

2023· article· en· W4323363531 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Social Research Methodology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsUniversity of Guelph
FundersCanadian Psychological Association
KeywordsFocus groupFocus (optics)Online research methodsData collectionOnline communityOnline discussionSociologyComputer sciencePsychologyPublic relationsWorld Wide WebPolitical scienceSocial science

Abstract

fetched live from OpenAlex

In response to concerns about the use of online focus groups, particularly around sensitive topics research, we provide two case examples of sensitive topics research that pivoted to online focus groups amid university ethics restrictions due to COVID-19 concerns. We begin by contextualizing the studies, one of which used the more traditional focus group method while the other employed a mix of focus groups and a variation on the World Café method, termed Community Cafés. We discuss issues like online platform choice (Microsoft Teams versus Zoom), security, and considerations for effective participant communication and connection. We demonstrate the effectiveness of online focus group data collection for sensitive research in two disciplines as well as the benefits to participants. We conclude by providing considerations and recommendations based on our own learnings for researchers wanting to conduct online focus group research on sensitive topics.

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)0.035
metaresearch head score (Gemma)0.088
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.088
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.879
GPT teacher head0.697
Teacher spread0.182 · 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