Translating cross‐language qualitative data in health professions education research: Is there an iceberg below the waterline?
Bibliographic record
Abstract
Health professions education research is an increasingly global community with culturally and linguistically diverse research teams who are challenged in how to communicate across cultures, contexts and languages. In today's diverse research landscape, language transcends its role as a mere means of communication and becomes a bridge that facilitates connections within research teams, between the team and its participants and between participants from diverse linguistic and cultural backgrounds and the scientific community. English as the 'lingua franca' is often seen as the international language of science and 'a prerequisite for scientific exchange'. This creates a language bias within the body of health professions education literature and involves methodological challenges for conducting research in non-English speaking contexts.
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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 | 0.385 | 0.568 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.040 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".