Développement d’une version canadienne-française du <i>Montgomery Borgatta Caregiver Burden Scale</i>
Bibliographic record
Abstract
Assessing the burden linked to caring for the frail elderly is becoming an important issue in rehabilitation. The purpose of this study was to translate/validate the Montgomery Borgatta Caregiver Burden Scale into French for use in Canada. This easy-to-use questionnaire evaluates aspects of burden such as objective, subjective stress and subjective demand burden. The instrument underwent: 1) parallel translation/back-translation; 2) expert's committee review leading to an experimental version; 3) pre-test to ensure wording clarity; and 4) study of psychometric properties with bilingual subjects (n = 27) and French-speaking subjects (n = 18). Results suggest that convergence between the original and the French versions is satisfactory for two of the three sub-scales of the MBCBS (ICC 0.83 & 0.96). The test-retest stability coefficients are also very good (ICC of 0.92 & 0.91), as is internal consistency (0.90, 0.66). The objective burden sub-scale correlates moderately with a measure of functional autonomy (SMAF). Results for the subjective burden scale linked to demand are, however, inadequate. All in all, two of the three sub-scales of the French-Canadian version of the Montgomery Borgatta Caregiver Burden Scale demonstrate adequate psychometric properties, thereby favouring its use in geriatric rehabilitation.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".