Exploring the translation process for multilingual implementation research studies: a collaborative autoethnography
Bibliographic record
Abstract
INTRODUCTION: In an increasingly globalised and interconnected world, evidence to evaluate complex interventions may be generated in multiple languages. However, despite its influence in shaping the evidence base, there is little literature explicitly connecting the translation process to the goals and processes of implementation research. This study aims to explore the processes and experience of an international implementation research team conducting a process evaluation of a complex intervention in Tibet Autonomous Region, China. METHODS: This study uses a collaborative autoethnographic approach to explore the translation process from Chinese or Tibetan to English of key stakeholder interview transcripts. In this approach, multiple researchers and translators contributed their reflections, and conducted joint analysis through dialogue, reflection and with consideration of multiple perspectives. Seven researchers involved with the translation process contributed their perspectives through in-depth interviews or written reflections and jointly analysed the resulting data. RESULTS: We describe the translation process, synthesise key challenges including developing a 'voice' and tone as a translator, conveying the depth of idioms across languages, and distance from the study context. We further offer lessons learnt including the importance of word banks with unified translations of words and phrases created iteratively during the translation process, the need to collaborate between translators and the introspective work necessary for translators to explore their positionality and reflexivity during the work. We then offer a summary of these learnings for other implementation research teams. CONCLUSION: Our findings emphasise that in order to ensure rigour in their work, implementation research teams using qualitative data should make concerted effort to consider both the translation process as well as its outcomes. Given the numerous multinational or multilingual implementation research studies using qualitative methods, there is a need for further consideration and reflection on the translation process.
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 | 0.079 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".