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Record W2302755114

Perception de la pharmacovigilance par les futurs pharmaciens hospitaliers belges, français, québécois et suisses

2014· article· fr· W2302755114 on OpenAlexaboutno aff
L. Cerruti, D Lebel, Thierry Van Hees, Olivier Bourdon, Pascal Bonnabry, Anne Spinewine, JD Hecq, JF Bussières

Bibliographic record

VenueORBi (University of Liège) · 2014
Typearticle
Languagefr
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePharmacovigilancePerceptionHumanitiesPsychologyPsychiatryDrugArt
DOInot available

Abstract

fetched live from OpenAlex

Comparer la perception de la pharmacovigilance par les rsidents en pharmacie hospitalire belges, franais, qubcois et suisses.tude descriptive prospective sous forme de sondage sur surveymonkey.comexpdi par courriel en mars 2014 229 rsidents en pharmacie hospitalire de 4 pays francophones : Belgique, France, Qubec et Suisse.Identification des variables pertinentes partir d'une revue de la littrature.Choix de 18 questions fermes et 1 question ouverte, organises en 5 sections : donnes dmographiques (2 questions), formation et pratique (8 questions), attitude face un EIM (6 questions), obstacles la dclaration d'EIM (1 question) et mesures pour amliorer le taux de dclaration (2 questions).Validation par pr-test de 5 rsidents en pharmacie hospitalire et relecture par un panel de pharmaciens hospitaliers.Prise en compte des suggestions pour modifier le questionnaire avant administration.Questionnaire et traitement des rponses strictement anonymes.Science d'observation et de surveillance des effets indsirables mdicamenteux (EIM), la pharmacovigilance repose sur la notification spontane des professionnels de sant.Intgre la pratique des pharmaciens, cette activit devrait tre une partie incontournable de la formation des rsidents en pharmacie hospitalire.

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)0.000
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: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.351
Teacher spread0.319 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueORBi (University of Liège)Same topicPharmacovigilance and Adverse Drug ReactionsFrench-language works237,207