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Record W4281852662 · doi:10.1136/bmjgh-2022-008674

Exploring the translation process for multilingual implementation research studies: a collaborative autoethnography

2022· article· en· W4281852662 on OpenAlexaff
Victoria Haldane, Betty Peiyi Li, Shiliang Ge, Jason Zekun Huang, Hongyu Huang, Losang Sadutshang, Zhitong Zhang, Pande Pasang, Jun Hu, Xiaolin Wei

Bibliographic record

VenueBMJ Global Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsRigourReflexivityProcess (computing)Context (archaeology)Computer scienceAutoethnographyIntrospectionQualitative researchSociologyTranslation studiesKnowledge managementPsychologyLinguisticsEpistemologySocial science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.944
GPT teacher head0.823
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2022
Admission routes1
Has abstractyes

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