Cross-Cultural Adaptation and Validation of a French Version of the Genetic Counseling Satisfaction Scale (GCSS) as an Outcome Measure of Genetic Counseling for Hereditary Breast and Ovarian Cancer
Bibliographic record
Abstract
(1) Background: The Genetic Counseling Satisfaction Scale (GCSS) is a widely used tool to evaluate patient satisfaction. To our knowledge, a validated French-language version of this tool is not yet available. This article reports on the cross-cultural adaptation and validation of a French version of the Genetic Counseling Satisfaction Scale (GCSS) to evaluate genetic counseling services for patient consultation in hereditary breast and ovarian cancer (HBOC). (2) Methods: The scale was culturally adapted following guidelines from Beaton et al. (2000). Cognitive interviews were conducted to ensure items were understood according to the intended meaning. The internal consistency, floor and ceiling effects, and testing of group differences were assessed using a sample of 172 patients who attended a pretest group genetic counseling session. (3) Results: Participants understood all items according to the intended meaning. The internal consistency was high for the total scale (0.90) and for the corrected item-to-total correlations (varying between 0.62 and 0.78). No floor or ceiling effects were observed. Group difference analyses generally followed expectations. (4) Conclusion: This process generated a French version of the GCSS that is clearly understood by patients, and has psychometric properties adequately in line those reported for its original English version.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".