Évaluation de la conformité à la politique de double vérification sur la feuille d’administration des médicaments en établissements de santé
Bibliographic record
Abstract
Resume Objectif : L’objectif de cet article est de presenter une evaluation pre-post de la conformite a la politique de double verification sur la feuille d’administration des medicaments en etablissements de sante. Mise en contexte : La double verification est definie comme le fait de confier a une personne la verification d’une tâche effectuee par une autre personne. Il peut s’agir de deux personnes ayant un meme titre d’emploi (p.ex. deux infirmieres) ou ayant des titres d’emploi differents (p.ex. un assistant-technique en pharmacie et un pharmacien). On reconnait que la double verification favorise une prestation securitaire de soins afin de reduire les risques d’incidents ou d’accidents. Apres la mise a jour de la politique de la double verification et sa diffusion aupres du personnel soignant, on note que la conformite globale est passee de 79 % en pre a 69 % en post. On observe une augmentation du nombre moyen de doubles verifications par patient, qui est passe de 1,0 en pre a 1,4 en post. Conclusion : Cette etude observationnelle pre-post indique une reduction non significative de la conformite globale a la politique de double verification dans un centre hospitalier tertiaire au Quebec, apres la mise a jour des medicaments cibles. Abstract Purpose: The purpose of this article is to present an evaluation of the conformity to a double verification policy for the medication administration sheet used in healthcare establishments before and after the implementation of this policy. Context: Double verification can be defined as entrusting to someone else the act of verifying a task performed by another person. It can involve two people having the same job title (for example, two nurses) or different job titles (for example, a pharmacy technician assistant and a pharmacist). Double verification favors safer delivery of care by reducing the risk of incidents and accidents. After the update of the double verification policy and its diffusion to healthcare staff, it was seen that global conformity to the policy went from 79% to 69%. An increase in the average number of double verifications per patient was observed (from 1.0 to 1.4) Conclusion: This observational study revealed a nonsignificant reduction in global conformity with respect to a double verification policy in a Quebec tertiary care hospital, after the update of targeted substances. Key Words: double verification; medication administration sheet; conformity.
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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 | 0.036 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".