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Record W7093298103 · doi:10.63209/2025.1581

Abréviations, symboles et désignations de dose à ne pas utiliser : observation ponctuelle de la pratique au CISSS de l’Outaouais

2025· article· W7093298103 on OpenAlexaffabout

Bibliographic record

VenuePharmactuel · 2025
Typearticle
Language
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversité du Québec en OutaouaisCégep de l'Outaouais
Fundersnot available
KeywordsJAMSSmoking preventionPulmonary medicine

Abstract

fetched live from OpenAlex

Objectif : Décrire le recours aux abréviations, symboles et désignations de doses à ne pas utiliser dans la rédaction des ordonnances pharmaceutiques par les prescripteurs du Centre intégré de santé et de services sociaux de l’Outaouais. Description de la problématique : À la suite des visites d’Agrément Canada, le Centre intégré de santé et de services sociaux de l’Outaouais ne parvenait pas à satisfaire tous les critères de conformité aux pratiques organisationnelles. Dans le cadre d’une démarche d’amélioration continue, le Département de pharmacie avait effectué un premier audit des ordonnances pharmaceutiques en 2022 qui avait révélé l’usage de certaines abréviations, de certains symboles et de certaines désignations de doses à ne pas utiliser sur plus de 50 % des ordonnances pharmaceutiques majoritairement rédigées par des médecins. Ce constat a conduit à plusieurs actions de sensibilisation auprès des prescripteurs. Résolution de la problématique : Bien que les actions de sensibilisation de 2022 n’aient pas produit les résultats escomptés, la direction de l’établissement a demandé un deuxième audit plus exhaustif portant sur un échantillon de 5200 feuilles d’ordonnances. Les résultats ont permis au Conseil des médecins, dentistes et pharmaciens, en collaboration avec la Direction des services professionnels et de la pertinence clinique et les chefs de département, d’entreprendre des interventions ciblées auprès des prescripteurs afin de réduire le recours aux symboles et aux abréviations les plus fréquentes, soit « ↑ » et « ↓ » et « S/C ». Conclusion : Cette étude a permis d’évaluer l’usage des abréviations et symboles à ne pas utiliser sur les ordonnances pharmaceutiques au Centre intégré de santé et de services sociaux de l’Outaouais, soulignant des améliorations dans certains centres, mais surtout la persistance de pratiques problématiques malgré les efforts pour les changer. Abstract Objective: To describe the use of «Do Not Use» abbreviations, symbols, and dose designations in prescribers pharmaceutical prescriptions at the Centre intégré de santé et de services sociaux de l’Outaouais. Problem Description: Following visits by Accreditation Canada, the CISSS de l’Outaouais failed to meet all the compliance criteria for organizational practices. As part of a continuous improvement initiative, the pharmacy department conducted an initial audit of pharmaceutical prescriptions in 2022, which revealed that over 50% of prescriptions—mostly written by physicians—contained “Do Not Use” abbreviations, symbols, or dose designations. This finding led to several awareness-raising actions targeting prescribers. Problem Resolution: Although the awareness efforts in 2022 did not yield the expected results, the institution's management requested a second, more comprehensive audit, covering a sample of 5,200 prescription sheets. The results enabled the Council of Physicians, Dentists, and Pharmacists, in collaboration with the Directorate of Professional Services and Clinical Relevance and department heads, to undertake targeted interventions with prescribers to reduce the use of the ↑ and ↓ symbols and the abbreviation S/C, which are the most commonly used. Conclusion: This study assessed the use of «Do Not Use» abbreviations and symbols in pharmaceutical prescriptions at the CISSS de l’Outaouais, highlighting improvements in certain centers, but more importantly, the persistence of problematic practices despite efforts to change them.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.069
GPT teacher head0.437
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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