Different Approaches to Cross-Lingual Focus Groups
Bibliographic record
Abstract
Focus groups are a useful data-generation strategy in qualitative health research when it is important to understand how social contexts shape participants’ health. However, when cross-lingual focus groups are conducted across cultural groups, and in languages in which the researcher is not fluent, questions regarding the usefulness and rigor of the findings can be raised. In this article, we will discuss three different approaches to cross-lingual focus groups used in a community-based participatory research project with pregnant and postpartum, African immigrant women in Alberta, Canada. In two approaches, we moderated focus groups in women’s mother tongue with the support of real-time interpreters, but in the first approach, audio recording was used and in the second approach, audio recording was not used. In the third approach, a bilingual moderator facilitated focus groups in women’s mother tongue, with transcription and translation of audio-recorded data upon completion of data generation. We will describe each approach in detail, including their advantages and challenges, and recontextualize what we have learned within the known literature. We expect the lessons learned in this project may assist others in planning and implementing cross-lingual focus groups, especially in the context of community-based participatory research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.174 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".