[Translation and adaptation to Spanish language of the quality of life questionnaire for celiac people called Canadian Celiac Health Survey].
Bibliographic record
Abstract
INTRODUCTION: To adapt and assess the quality of life questionnaire called Canadian Celiac Health Survey (CCHS). OBJECTIVE: To translate and adapt CCHS questionnaire to be used by the Spanish-speaking population since it is a specific questionnaire for celiac disease. METHOD: To adapt the CCHS, which consists of 76 items divided into 11 different sections, was performed using translation-back-translation method and after being reviewed and agreed proceeded to conduct a pilot test with 25 people with celiac disease, individually and a member of the research group to assess the understanding of the items and their sections. The contributions were introduced, setting the final questionnaire. RESULTS: The greatest difficulty in the translation in question occurred where there were active and trade names of drugs, opting for it to those marketed nationwide. On the other hand, for the pilot study questionnaire showed a good value of the naturalness of understanding with values between 8.4 and 10.0. CONCLUSIONS: The specific tool CHCS allow the use of a questionnaire that can be used by the Spanish speaking population studies, clinical trials or health professional practice everyday, allowing a better understanding of the health of celiacs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".