Reflections on the process of translation and cultural adaptation of an instrument to investigate taste and smell changes in adults with cancer
Bibliographic record
Abstract
Taste and smell changes are common and distressing symptoms in patients with cancer, which may contribute to decreased nutritional intake leading to malnutrition and reduced quality of life. Evidence-based knowledge available to healthcare staff regarding dietary counselling of patients with taste and smell changes is lacking. To be able to develop advice to patients, these symptoms need to be characterised and assessed. The Taste and Smell Survey (TSS) is a 16-item questionnaire in English, which has been used in Canada to investigate self-perceived changes in taste and smell reported by patients with cancer. As no equivalent instrument exists in Swedish, we therefore translated the TSS. This article describes and discusses experiences of using a 5-step process for translation and cultural adaptation of the TSS. Each of the five steps was found to elicit different, essential information contributing to the enhancement of the translation and building further upon refinements of the previous steps. Using a structured, multistep approach to translation and cultural adaptation, we have produced a robust instrument to investigate taste and smell changes specifically adapted for use in the Swedish language and culture.
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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 | 0.235 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".