Cross‐cultural equivalence in translations of the oral health impact profile
Bibliographic record
Abstract
The Oral Health Impact Profile (OHIP) has been translated for comparisons across cultural boundaries. This report on a systematic search of literature published between 1994 and 2014 aims to identify an acceptable method of translating psychometric instruments for cross-cultural equivalence, and how they were used to translate the OHIP. An electronic search used the keywords 'cultural adaptation', 'validation', 'Oral Health Impact Profile' and 'OHIP' in MEDLINE and EMBASE databases supplemented by reference links and grey literature. It included papers on methods of cross-cultural translation and translations of the OHIP for dentulous adults and adolescents, and excluded papers without translational details or limited to specific disorders. The search identified eight steps to cross-cultural equivalence, and 36 (plus three supplemental) translations of the OHIP. The steps involve assessment of (i) forward/backward translation by committee, (ii) constructs, (iii) item interpretations, (iv) interval scales, (v) convergent validity, (vi) discriminant validity, (vii) responsiveness to clinical change and (viii) pilot tests. Most (>60%) of the translations involved forward/backward translation by committee, item interpretations, interval scales, convergence, discrimination and pilot tests, but fewer assessed the underlying theory (47%) or responsiveness to clinical change (28%). An acceptable method for translating quality of life-related psychometric instruments for cross-cultural equivalence has eight procedural steps, and most of the 36 OHIP translations involved at least five of the steps. Only translations to Saudi Arabian Arabic, Chinese Mandarin, German and Japanese used all eight steps to claim cultural equivalence with the original OHIP.
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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 | 0.133 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".