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Record W1655040711 · doi:10.1093/ajhp/60.23.2475

Delivery of multimedia educational presentations via streaming media

2003· article· en· W1655040711 on OpenAlexaffabout
Robert M. Balen, Peter J. Jewesson

Bibliographic record

VenueAmerican Journal of Health-System Pharmacy · 2003
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsVancouver General HospitalVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsPharmacyLibrary scienceUnit (ring theory)Service (business)MedicineSociologyPsychologyFamily medicineComputer science

Abstract

fetched live from OpenAlex

Journal Article Delivery of multimedia educational presentations via streaming media Get access Elaine Chong, B.SC.PHARM., Elaine Chong, B.SC.PHARM. Manager, Clinical Services Virtual Learning, Inc., Toronto, ON, Canada; when this article was written she was a doctor of pharmacy degree student, Faculty of Pharmaceutical Sciences, University of British Columbia (UBC), Vancouver, BC, Canada Search for other works by this author on: Oxford Academic Google Scholar Robert M. Balen, Pharm.D., Robert M. Balen, Pharm.D. Informatics Coordinator Pharmaceutical Sciences Clinical Service Unit, Vancouver General Hospital (VGH), Vancouver Coastal Health Authority (VCHA) Address correspondence to Dr. Balen at the Pharmaceutical Sciences Clinical Service Unit, Vancouver General Hospital, 855 West 12th Avenue, Vancouver, BC V5Z 1M9, Canada rbalen@interchange.ubc.ca. Search for other works by this author on: Oxford Academic Google Scholar Peter J. Jewesson, Ph.D. Peter J. Jewesson, Ph.D. Professor Faculty of Pharmaceutical Sciences, UBC, and Clinical Director, Pharmaceutical Sciences Clinical Service Unit, VGH, VCHA Search for other works by this author on: Oxford Academic Google Scholar American Journal of Health-System Pharmacy, Volume 60, Issue 23, 1 December 2003, Pages 2475–2478, https://doi.org/10.1093/ajhp/60.23.2475 Published: 01 December 2003

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)0.001
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 score0.386
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.110
GPT teacher head0.446
Teacher spread0.336 · 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

Citations7
Published2003
Admission routes2
Has abstractyes

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Same venueAmerican Journal of Health-System PharmacySame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207