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Record W7093300383 · doi:10.63209/2025.1565

Description de l’organisation, des perceptions et des actions des pharmaciens hospitaliers exposés aux pénuries de médicaments au Québec

2025· article· W7093300383 on OpenAlexafffundabout

Bibliographic record

VenuePharmactuel · 2025
Typearticle
Language
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
FundersCentre Hospitalier Universitaire de QuébecUniversité Laval
KeywordsWork (physics)Occupational trainingOrganizational changeQuantitative methodology

Abstract

fetched live from OpenAlex

Objectif : Décrire l’organisation, les perceptions et les actions des pharmaciens hospitaliers exposés aux pénuries de médicaments. Méthode : Étude descriptive transversale menée auprès des pharmaciens membres du comité des utilisateurs du Centre d’acquisitions gouvernementales. Un questionnaire en ligne de 49 questions a été créé (SurveyMonkey, Palo Alto, CA, ÉU). L’invitation a été transmise le 16 janvier 2024 par courriel et sur l’équipe Teams du Centre d’acquisitions gouvernementales. Seules des statistiques descriptives ont été compilées (moyenne ± écart-type, proportions). Résultats : Vingt-sept pharmaciens (38 %, 27/71) représentant 20 établissements de santé du Québec (67 %, 20/30) ont rempli le sondage. La médiane de la charge de travail hebdomadaire estimée par les répondants était de 6 heures [min. = 3,5; max. = 9,0] de temps de pharmaciens combinée à 8 heures [min. = 4,0; max. = 19,0] de temps d’assistants techniques séniors en pharmacie. Les proportions de répondants se conformant aux barèmes de stockage minimal étaient les suivantes : 93 % (25/27) pour les médicaments critiques (90 jours), 89 % (24/27) pour les médicaments d’oncologie (30 jours), 81 % (22/27) pour la plupart des médicaments (60 jours) et 70 % (19/27) pour les solutés (60 jours). Les répondants ont eu recours à 26 actions correctrices pharmaceutiques potentielles dans des proportions variant de 37 % à 100 %. Conclusion : Dans la littérature, les pénuries de médicaments ont des répercussions connues sur le travail des cliniciens et les soins prodigués aux patients. Cette étude met en évidence le fait que les pharmaciens hospitaliers sont très préoccupés par les pénuries de médicaments et que ces pénuries ont un effet important sur la charge de travail en établissement de santé. Abstract Objective: To describe the organization, perceptions, and actions of hospital pharmacists facing drug shortages. Method: A cross-sectional descriptive study was conducted among pharmacists who are members of the user committee of the Centre d’acquisitions gouvernementales. A 49-question online survey was created (SurveyMonkey, Palo Alto, CA, USA). The invitation was sent on January 16, 2024, via email and the Centre d’acquisitions gouvernementales’ Teams group. Only descriptive statistics were compiled (mean ± standard deviation, proportions). Results: Twenty-seven pharmacists (38%, 27/71) representing 20 healthcare institutions in Quebec (67%, 20/30) completed the survey. The median estimated weekly workload reported by respondents was 6 hours [min. = 3.5; max. = 9.0] of pharmacist time combined with 8 hours [min. = 4.0; max. = 19.0] of senior pharmacy technical assistant time. The proportions of respondents complying with minimum stock level guidelines were as follows: 93% (25/27) for critical drugs (90 days), 89% (24/27) for oncology drugs (30 days), 81% (22/27) for most drugs (60 days), and 70% (19/27) for IV solutions (60 days). Respondents reported using 26 potential pharmaceutical corrective actions in proportions ranging from 37% to 100%. Conclusion: According to the literature, drug shortages have known impacts on clinicians’ work and patient care. This study highlights that hospital pharmacists are highly concerned about drug shortages and that these shortages significantly affect the workload in healthcare institutions.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.213
GPT teacher head0.473
Teacher spread0.260 · 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; both teacher heads agree on what is shown here.

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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