The Validity and Reliability Test of the Indonesian Version of Gastroesophageal Reflux Disease Quality of Life (GERD-QOL) Questionnaire.
Bibliographic record
Abstract
AIM: to obtain a valid and reliable GERD-QOL questionnaire for Indonesian application. METHODS: at the initial stage, the GERD-QOL questionnaire was first translated into Indonesian language and the translated questionnaire was subsequently translated back into the original language (back-to-back translation). The results were evaluated by the researcher team and therefore, an Indonesian version of GERD-QOL questionnaire was developed. Ninety-one patients who had been clinically diagnosed with GERD based on the Montreal criteria were interviewed using the Indonesian version of GERD-QOL questionnaire and the SF 36 questionnaire. The validity was evaluated using a method of construct validity and external validity, and reliability can be tested by the method of internal consistency and test retest. RESULTS: the Indonesian version of GERD-QOL questionnaire had a good internal consistency reliability with a Cronbach Alpha of 0.687-0.842 and a good test retest reliability with an intra-class correlation coefficient of 0.756-0.936; p<0.05). The questionnaire had also been demonstrated to have a good validity with a proven high correlation to each question of SF-36 (p<0.05). CONCLUSION: the Indonesian version of GERD-QOL questionnaire has been proven valid and reliable to evaluate the quality of life of GERD patients.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".