Évaluation de la version française d’une formation en ligne sur l’appropriation d’une échelle d’évaluation de la douleur pédiatrique
Bibliographic record
Abstract
Context: There is no French-language training to educate nurses on the use of the Face, Legs, Activity, Cry, Consolability (FLACC) pain scale, whose scores guide the treatment of pediatric pain. Aims: The aim of this study was to evaluate a French online training program for the FLACC scale offered to Francophone undergraduate nursing students. Methods: Online training was offered to nursing students enrolled in a pediatric nursing course. Participants completed online questionnaires pre- and post-training to assess their perception of their knowledge and confidence, the accuracy of their pain assessment scores, as well as the usefulness and user-friendliness of the training. Results: The FLACC online training improved students’ perceived knowledge (p = 0.0004) and confidence (p = 0.0053) in the FLACC pediatric pain scale. Students’ accuracy of severe pain assessment scores significantly improved (p = 0.0159) and slightly improved for moderate pain (p = 0.6363). However, accuracy for mild pain assessment was slightly decreased post-training (p = 0.7686). Discussion: An improvement of the quality of videos linked to mild pain, and the quantity of videos for all levels of pain, is required for this study to be replicated among a larger sample. Conclusion: The online training fills the gap in nurses’ lack of knowledge about the use of the FLACC pain scale and improves access to quality training in French.
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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 | 0.089 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".