Strengthening data quality in studies of migrants not fluent in host languages: A canadian example with reproductive health questionnaires
Bibliographic record
Abstract
The need to collect health data from refugees and asylum seekers often requires that questionnaires be translated. Verifying the clarity, meaning, and acceptability of translated questionnaires with monolingual persons, individuals from the target population who primarily speak and understand only the test language, is one important step in the translation process. Reproductive health questionnaires were tested with persons monolingual in Hindi, Tamil, Urdu, Spanish, and French. Testing revealed problematic questions and how culture, education, and migration experience can affect perceptions of questions. Bilingual liaisons from the communities of interest facilitated recruitment of participants, but liaisons' vulnerable status and lack of familiarity with research posed challenges to the testing process. When conducting monolingual testing it is important to: carefully select liaisons (consider their gender, host-language fluency, knowledge of research processes, and comfort with the subject matter of the research); recruit monolingual persons with characteristics representative of the research population; ensure adequate researcher involvement in all aspects of the testing process to triangulate data collection from various sources.
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.
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 | 0.641 | 0.610 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".