French Adaptation of the Strengths Use Scale
Bibliographic record
Abstract
Background: Positive psychology focuses on enhancing attitudes and behaviors that support well-being, with a key pillar being the use of psychological strengths for optimal functioning. This is linked to positive outcomes such as increased happiness and life satisfaction. Objective: This study aimed to evaluate the psychometric validity of the French adaptation of the Strengths Use Scale (SUS), a self-report tool measuring how individuals use their strengths in daily life. The original SUS, developed by Govindji and Linley (2007), has not been thoroughly assessed across languages and cultures. Method: = 1397). After removing cases with missing data, exploratory factor analysis (EFA) was conducted on a subsample to establish the optimal factor structure. Confirmatory factor analysis (CFA) was then performed to assess the factor structure's goodness-of-fit. Results: Both EFA and CFA supported a unidimensional structure of the scale. The French SUS demonstrated good internal consistency (α = .94). The one-factor model yielded an RMSEA of .122, indicating some model misspecification. However, allowing residuals of some items to covary improved the model fit (RMSEA = .077). Conclusion: The adapted French SUS exhibits similar properties to the original and presents no new consistency issues. This study contributes to adapting and validating the SUS in French for research and clinical practice. Future research should focus on developing a shorter version by eliminating redundancies and adapting the scale for children to evaluate positive psychology interventions' efficacy in youth.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".