Canadian English translation and linguistic validation of the 13-MD to measure global health-related quality of life
Bibliographic record
Abstract
The 13-MD is a new instrument designed to measure more globally the various aspects of the health-related quality of life. Its structure is balanced around physical, mental, and social aspects of health. To translate the 13-MD into Canadian English and to ensure that it is conceptually equivalent to the original version in Canadian French. Forward and back translations were conducted. A linguistic validation was performed in both Canadian French and Canadian English following an iterative process. This validation was conducted with 15 participants in each group (French and English speakers) using face-to-face cognitive debriefing interviews. This process was done in accordance with academic standards. The two forward translations resulted in 35.8% of identical sentences (59/165). Back translation indicated that 83.6% of the sentences were identical or almost identical to the original Canadian French version. The review of the back translation led to a few changes in the reconciled forward translation (4/165) and the original version (11/165), while the linguistic validation process led to 24 changes over a possibility of 165 sentences in the Canadian English version and 6 over 165 in the Canadian French version. Most changes provided were minimal and were done to ensure a better understanding of the 13-MD. The translation and linguistic validation processes were successful in creating a valid 13-MD in Canadian English (13-MD-CE) that is conceptually equivalent to the original version.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".