French Translation of the Multidimensional Pain Inventory: <i>L’inventaire multidimensionnel de la douleur</i>
Bibliographic record
Abstract
BACKGROUND: The Multidimensional Pain Inventory (MPI) is a widely used tool in the evaluation of pain conditions. This questionnaire has been translated and validated in multiple languages. However, there is no validated French-language version available for clinicians and researchers interested in evaluating people living with pain. OBJECTIVES: The main objective of the present project was to make available a validated French-language evaluation tool for the cognitive, behavioural and emotional aspects of pain. METHODS: Following a reverse translation of the MPI, a French-language version of the questionnaire, the Inventaire multidimensionnel de la douleur, that was presented to 227 participants living with chronic pain, was obtained. These participants were all involved in a rehabilitation program in four different settings. A series of exploratory and confirmatory factor analyses was executed. RESULTS AND CONCLUSIONS: Although three items were removed from the original version of the MPI, the three sections of the Inventaire multidimensionnel de la douleur had good psychometric properties. The results concerning the questionnaire's structure were very similar to those obtained with the original tool and during its translation into other languages. People wishing to evaluate pain in French-speaking populations now have access to a French-language version of the MPI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".