A French adaptation of the Overt Behaviour Scale (OBS) measuring challenging behaviours following acquired brain injury: The Échelle des comportements observables (ÉCO)
Bibliographic record
Abstract
PURPOSE: To develop a French version of the Overt Behaviour Scale (OBS) and examine some of its psychometric properties. METHODS: The scale was adapted and validated according to standard guidelines for cross-cultural adaptation of questionnaires (Échelle des comportements observables; ÉCO). The reliability and construct validity of the ÉCO were studied among 29 inpatients and outpatients who sustained an acquired brain injury. The instruments were administered by 12 clinicians located at eight rehabilitation centres and the local brain injury association. RESULTS: The ÉCO provided behaviour profile descriptives much like the original scale. It showed excellent reliability and good convergent and divergent validity, as reflected by significant associations with other measures that contained similar behavioural items and by the absence of signification correlations with broader constructs such as physical and cognitive abilities. CONCLUSION: This study provides evidence that the ÉCO behaves much like the original OBS, has promising initial findings with respect to reliability and validity and is a valuable research and clinical instrument to assess the severity and typology of challenging behaviour after an acquired brain injury and to monitor the evolution of behaviours after intervention in French and bilingual communities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".