Challenges When Translating and Culturally Adapting a Measurement Instrument: The Suitability and Comprehensibility of Materials (SAM+CAM)
Bibliographic record
Abstract
There is evidence that low suitability and comprehensibility of printed education materials (PEMs) affects patients' and relatives' ability to read and comprehend information. However, few instruments measure the suitability of written information, and none exist in the Swedish language. The aim was to describe the translation and adaptation of the Suitability and Comprehensibility of Materials (SAM+CAM) instrument into the Swedish language and health care context and to explore challenges related to this process. The SAM+CAM instrument was translated and culturally adapted in five steps: forward translation, synthesis, back translation, expert review, and pretests. Differences were found when translating and culturally adapting the SAM+CAM instrument in the areas of semantic, idiomatic, and experiences. Participants revealed several clarity inconsistencies between items. They also identified linguistic differences and unfamiliar wording; they found that the instrument was perplexing to use and lacked knowledge regarding the specific health care areas in the examined PEMs. The cultural perspective is a significant factor that influences the usability of PEMs. Therefore, expert groups of participants are useful when adapting instruments to different cultures. The Swedish SAM+CAM instrument requires experienced and highly qualified raters.
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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 | 0.249 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".