Canadian French Translation of The Couples Satisfaction Index: A Pre-Validation Pilot Study Exploring Men’s Perspective
Bibliographic record
Abstract
Abstract Background It is imperative for health care professionals to have access to valid and reliable tools to evaluate the quality of romantic relationships, as it has been repeatedly shown to be related to psychological and physical health outcomes. The Couples Satisfaction Index (CSI) was developed to address the shortcomings of the most widely cited measures of satisfaction by increasing precision and power of measurement. However, the use of this questionnaire is limited due to a lack of translated versions. The purpose of the current study was therefore to translate the CSI to Canadian French (CanFrench-CSI) and to evaluate its reliability in a sample of male Canadians. Methods The CSI questionnaire was adapted and translated into Canadian French using a forward-backward approach. Its reliability was then evaluated by means of Cronbach’s alpha internal consistency coefficients, corrected item-total correlation coefficients, and a test-retest intraclass correlation coefficient at a two-week interval. Results Data from 53 men in committed relationships were analyzed. The French adaptation of the CSI demonstrated satisfactory internal consistency and test-retest reliability. Conclusions Overall, the results provided evidence supporting the translation consistency and reliability of the CanFrench-CSI, and thus, paved the way for further research on romantic relationships and health outcomes among French-speaking individuals.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.007 | 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".