Multimorbidity Treatment Burden Questionnaire (MTBQ): Translation, Cultural Adaptation, and Validation in French-Canadian
Bibliographic record
Abstract
Reliable treatment burden measures are needed given the aging population and the associated increase in multimorbidity and polypharmacy. Treatment burden is defined as the effort to care for one's health and the resulting impact on one's daily life. This study aimed to translate the Multimorbidity Treatment Burden Questionnaire (MTBQ) for French-Canadians and assess its reliability and validity. The MTBQ was translated and tested with cognitive debriefing interviews, and the French version (MTBQ-F) was then administered 2 times among 105 participants. Reliability and validity were examined using the intra-class correlation coefficient (ICC), Cronbach's alpha, and Spearman's correlations. The median global MTBQ-F scores were 32.69 (interquartile range [IQR]: 21.15-48.08) and 30.77 (IQR: 21.15-46.15) for the first and second administrations, respectively. Test-retest (ICC: 0.73; 95% CI: 0.63-0.81) and internal consistency reliability (Cronbach's alpha: 0.80) were good. There was a moderate positive correlation between the MTBQ-F score and the number of self-reported conditions (rho: 0.28). This valid instrument could identify patients experiencing a high treatment burden and assess the impact of interventions among them.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".