Cross-cultural adaptation of Delphi definitions of low back pain prevalence in French (Delphi DOLBaPP-F)
Bibliographic record
Abstract
Aim: The high heterogeneity in the definitions of low back pain encountered in the literature has led to the development of standardized definitions of this condition called “Delphi definitions of low back pain prevalence (Delphi DOLBaPP)” by a group of international researchers. In order to be widely used, these definitions need to be adapted according to the cultural and linguistic context. The aim of this work was to perform the cross-cultural adaptation of the Delphi DOLBaPP definitions in Quebecc French and to pre-test them among French-speaking adults. Methods: In order to enable practical use of the Delphi DOLBaPP definitions in different contexts, their presentation was adapted in the form of a questionnaire (referred to as the “Delphi DOLBaPP questionnaire”). The process of cross-cultural adaptation of the Delphi DOLBaPP questionnaire in French was conducted according to the most recognized recommendations for the cultural adaptation of measuring instruments. The resulting questionnaire and an evaluation form were then submitted to a sample of 82 adults. Results: A total of 41 participants (50.0%) reported low back pain. A high proportion of participants (89.0%) stated that it took them less than 5 minutes to complete the questionnaire. More than 62.0% of them did not find any question poorly worded or confusing. Nearly 80.0% of the participants found the questionnaire easy to understand. The cross-cultural adaptation process suggested minor modifications to the original Delphi DOLBaPP questionnaire. Conclusions: This study has produced a cross-cultural adaptation of the Delphi DOLBaPP questionnaire in Quebec French that will enable French-speaking populations to share the benefits of using standardized definitions of low back pain in epidemiological studies.
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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 | 0.083 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".