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Record W4412957666 · doi:10.4212/cjhp.3748

Tamper-Proofing Multidose Medications in Hospitals: High-Value, Low-Effort Interventions to Reduce Medication Waste

2025· article· en· W4412957666 on OpenAlexaffvenueabout
Simroop Ladhar, Dionzie Ong, Alfie Chung, Isla Drummond, Aaron M Tejani

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsPsychological interventionMedicineValue (mathematics)Medication adherenceIntensive care medicineMedical emergencyNursingComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Background: The Canadian health care sector contributes 4.6% of national greenhouse gas emissions, with medications accounting for 25% of that amount. Reducing waste from high-cost multidose items such as eye drops and inhalers can lower environmental and health care costs. Objectives: To evaluate tamper-proofing practices in hospital pharmacy departments in British Columbia and to explore opportunities for standardization to reduce medication waste. Methods: Site visits were conducted at 13 hospitals across 3 health authorities in British Columbia—Vancouver Coastal Health, Providence Health Care, and Fraser Health—to gather data on tamper-proofing practices in hospital pharmacy departments. Results: Methods of tamper-proofing varied across departments. Key recommendations for improvement include the implementation of adhesive stickers or other tamper-evident features, standardization of the placement of tamper-evident adhesive stickers, prioritization of patient-specific medications for tamper-proofing, and development of and adherence to policies/procedures for most multidose medications before they leave the pharmacy. Conclusions: Standardizing tamper-proofing practices can reduce medication waste and environmental impact, with potential for broader adoption across hospitals. Keywords: tamper-proofing, medication waste, multidose medications, standardization RÉSUMÉ Contexte : Le secteur des soins de santé canadien est responsable de 4,6 % des émissions de gaz à effet de serre nationales et les médicaments représentent 25 % de ce chiffre. La réduction du gaspillage des articles multidoses coûteux tels que les gouttes pour les yeux et les inhalateurs peut diminuer les coûts environnementaux et de santé. Objectifs : Évaluer les pratiques en matière d’inviolabilité dans les départements de pharmacie hospitalière en Colombie-Britannique et explorer les possibilités de standardisation pour réduire le gaspillage de médicaments. Méthodologie : Des visites des lieux ont été réalisées dans 13 hôpitaux répartis sur 3 autorités sanitaires en Colombie-Britannique – Vancouver Coastal Health, Providence Health Care et Fraser Health – afin de collecter des données sur les pratiques en matière d’inviolabilité dans les départements de pharmacie hospitalière. Résultats : Les méthodes d’inviolabilité variaient d’un département à l’autre. Les principales recommandations d’amélioration comprennent la mise en place d’autocollants ou d’autres témoins d’intégrité, la standardisation du positionnement de ces témoins d’intégrité, la priorisation des médicaments spécifiques aux patients pour l’inviolabilité, ainsi que l’élaboration et le respect de politiques/procédures pour la plupart des médicaments multidoses avant leur sortie de la pharmacie. Conclusions : La normalisation des pratiques d’inviolabilité peut réduire le gaspillage de médicaments et l’impact environnemental, avec un potentiel d’une adoption plus large dans les hôpitaux. Mots-clés : inviolabilité, gaspillage des médicaments, médicaments multidoses, standardisation

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.411
Teacher spread0.383 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes3
Has abstractyes

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