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Record W2118563806 · doi:10.5430/jct.v2n1p63

Asynchronous Versus Synchronous Learning in Pharmacy Education

2013· article· en· W2118563806 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Curriculum and Teaching · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacyAsynchronous communicationPharmacy educationMedical educationAsynchronous learningComputer scienceDistance educationPharmacy practiceTeaching methodMedicineMathematics educationPsychologyNursingSynchronous learningCooperative learning

Abstract

fetched live from OpenAlex

Objective: To better understand the technology being used today in pharmacy education through a review of thecurrent methodologies being employed at various institutions. Also, to discuss the benefits and difficulties ofasynchronous and synchronous methodologies, which are being utilized at both traditional and distance educationcampuses.Setting: Colleges of Pharmacy across the countrySummary: Pharmacy education has seen dramatic changes over the past decade. With the explosion of newtechnologies come new methods for teaching practitioners. This article describes the various methods being usedtoday to teach our practitioners and the advantages and disadvantages of each method.Conclusion: The authors conclude that using a blended method of teaching through both asynchronous andsynchronous learning produces practitioners ready to take on the new challenges experienced by today’s pharmacists.

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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.005
GPT teacher head0.248
Teacher spread0.243 · 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