Translation of research instruments : research processes, pitfalls and challenges
Bibliographic record
Abstract
Multilingual and multi-ethnic societies are becoming the norm in the era of globalisation. Given the cultural diversity and multiple languages spoken in many countries, healthcare researchers (including nurses) are challenged to use psychometrically sound research instruments that are culturally and linguistically sensitive. Most psychometrically sound research instruments have been developed and their properties assessed in English-speaking populations. A literature review was performed to understand the process of translation, use of qualitative and quantitative methods to assess the quality of translation, and lastly, to identify strategies to overcome the challenges of the translation process. One-way translation was observed to be the most utilised method. Translation methods and processes have many challenges, but applying relevant strategies could reduce errors and pitfalls.
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 | 0.710 | 0.798 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".