Développement et validation d'un questionnaire pour la création d'un registre des commotions cérébrales au Québec
Bibliographic record
Abstract
Objective: To develop and validate a questionnaire for creating a registry of concussions.The eventual registry will be used by healthcare professionals encountering cases of concussions outside emergency departments in Quebec.This objective is divided into two sub-objectives: 1. Develop and create the questions that will constitute the questionnaire used for a potential creation of registry.2.Validate the use of the questionnaire with a small group of athletic therapists (also known as athletic trainers in USA) considered experts in the field of concussions.Methods: The questionnaire was created using the Qualtrics platform.The questionnaire was completed by athletic therapists working closely with concussion cases to validate its use and its viability.Data were collected using an evaluation questionnaire to analyze the clarity and relevance of the questions.Results: Raters evaluated the use of the questionnaire as follow: 37,5% indicated VERY EASY, 37,5% EASY, and 25,0% CORRECT.The statistical analysis for clarity and relevance of the questions found within the data gathering tool for the creation of a potential registry revealed high agreement levels: A. For clarity: Gwet's AC1 coefficient equals 0,976 for raters 1,2,3,4,5 and 8 each.B. For relevance: Gwet's AC1 coefficient equals 0,920 for raters 1,2,3,5 and 8 each. Conclusion:The results of our study demonstrates that the proposed questionnaire can be used in a concussion registry to ensure the identification of concussion cases presented to the athletic therapist in the field or in a private clinic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".