Different Approaches to Cross-Lingual Focus Groups
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it