Développement et validation de la version canadienne-française de l’échelle de Satisfaction des Adolescents de la gestion de la Douleur postopératoire – Scoliose idiopathique (SAD-S)
Bibliographic record
Abstract
Background: Spinal fusion for scoliosis generates moderate to severe pain intensity. There are currently no instruments available to measure adolescents’ satisfaction regarding post-spinal fusion pain management. Aims: To develop and validate a scale on satisfaction of adolescents regarding pain management following spinal fusion for scoliosis. Methods: A methodological design was used to develop and validate the French-Canadian scale “Satisfaction des Adolescents de la gestion de la Douleur postopératoire – Scoliose idiopathique (SAD-S)”. A modified Delphi method, with seven healthcare professionals and 10 adolescents, was used to establish content validity of the SAD-S. A pre-test of the scale was conducted with 10 adolescents post-spinal fusion. The final version of the scale was validated through a pilot study with 98 adolescents following their surgery. Results: The SAD-S scale includes a total of 13 items. Principal component analysis yielded a two-factor structure (2 subscales): 1) Pain management education and 2) Education regarding medication. These two factors explained 47,8% of the total variance for satisfaction. A Cronbach’s alpha of 0,84 was obtained for internal consistency. Conclusion: Validation of the SAD-S scale showed that it has good psychometric properties with this population. Further validation is required with a larger sample to pursue its validation.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".