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Record W4411928096 · doi:10.4212/cjhp.3676

Adoption of Closed-System Drug Transfer Devices: Effectiveness in Reducing Occupational Exposure to Hazardous Drugs and the Change Management Process.

2025· article· en· W4411928096 on OpenAlexaff
Chun‐Yip Hon, Jackie Ellis, Rita Ciconte, A. Dana Ménard

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSafe Handling of Antineoplastic Drugs
Canadian institutionsUniversity of British ColumbiaUniversity of WindsorFraser HealthToronto Public Health
Fundersnot available
KeywordsHazardous wasteProcess (computing)DrugRisk analysis (engineering)BusinessProcess managementMedicinePharmacologyComputer scienceEngineeringWaste management

Abstract

fetched live from OpenAlex

Background: Closed-system drug transfer devices (CSTDs) are known to be effective in reducing hazardous drug contamination and, in turn, the risk of exposure for health care workers. In response, the Fraser Health Authority in British Columbia had plans to introduce CSTDs into practice. Objectives: To confirm the effectiveness of CSTDs in reducing hazardous drug contamination and to understand health care workers' perspectives regarding the change management process for CSTD implementation. Methods: . To understand the change management process, health care workers at the same departments (as those where wipe samples were collected) were surveyed. Results: ). About 50 individuals responded to each question of the survey, and respondents had generally positive comments regarding the transition to CSTDs. Nevertheless, suggestions for improvement included offering various forms of training (e.g., online video, hands-on sessions) and ensuring ongoing communication. Conclusions: CSTDs were confirmed to be effective in reducing surface contamination levels, and the change management process employed by the health authority appeared to be well received.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.420
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.325
Teacher spread0.293 · 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 routes1
Has abstractyes

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