Bibliographic record
Abstract
Resume Objectif : Rapporter l’experience du Centre hospitalier universitaire de Sherbrooke visant a eliminer les abreviations dangereuses. Description de la problematique : Agrement Canada exige de ne plus utiliser d’abreviations ambigues et dangereuses dans les documents relatifs aux medicaments, ce qui implique l’etroite collaboration entre les prescripteurs et les personnes autorisees a prendre des ordonnances telephoniques. Les approches possibles vont de la formation a la fonction forcee en passant par le renforcement. Il n’existe pas de consensus sur l’efficacite comparative des approches de formation versus de renforcement. Description de l’approche : La campagne du centre hospitalier universitaire de Sherbrooke repose principalement sur la mise au courant des risques lies a l’utilisation des abreviations dangereuses. Elle privilegie les interactions directes avec les prescripteurs et se concentre sur de courtes presentations au sein des services et departements medicaux. Elle comporte egalement la publication de billets dans le journal intrahospitalier sur les differentes abreviations dangereuses, des journees de retroactions offertes aux prescripteurs utilisant des abreviations dangereuses et prevoit des mesures de renforcement selon les resultats atteints. Conclusion : La proportion d’abreviations dangereuses est passee de 20 % a 11 % sur une periode d’un peu plus de deux ans a la suite de notre campagne d’information. La proportion des ordonnances conformes, dont la designation de dose s’exprime en unites, est passee du tiers aux deux tiers durant la campagne. Des mesures de renforcement plus specifiques seront entreprises a l’intention des prescripteurs ou groupes de prescripteurs generant la plus grande proportion d’abreviations. Abstract Objective: To report the experience of implementing a quality assurance program for eliminating the use of dangerous abbreviations in an academic medical center. Problem description: Accreditation Canada identifies the needs for hospitals to eliminate dangerous abbreviations, symbols and dose designations. Different approaches include training and forcing functions while using reinforcement. There is no consensus on the effectiveness of using education versus reinforcement. Description of the approach: The campaign at the academic medical center focuses on raising awareness of the risks involved using dangerous abbreviations. Different methods were used such as direct interactions with prescribers along with short presentations to medical staff, dissemination of materials in the hospital newspaper on dangerous abbreviations and organization of feedback sessions to prescribers using dangerous abbreviations. Conclusion: The proportion of dangerous abbreviations decreased from 20% to 11% over a period of two years. The proportion of prescriptions that complied with standards, where dose designation was expressed in units, increased from one-third to two-thirds during the campaign. Key words: Dangerous abbreviation, campaign, hospital, medication
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".