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Record W2117490110 · doi:10.7771/1541-5015.1324

Drugs, Devices, and Desires: A Problem-based Learning Course in the History of Medicine

2013· article· en· W2117490110 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInterdisciplinary Journal of Problem-based Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsArchivistProblem-based learningMedical educationCourse (navigation)Component (thermodynamics)PsychologyMathematics educationComputer scienceMedicineEngineeringLibrary science

Abstract

fetched live from OpenAlex

Problem-based learning (PBL) is well suited for courses in the history of medicine, where multiple perspectives exist and information has to be gleaned from different sources. A student, an archivist, and a teacher offer three perspectives about a senior level course where students explored the antecedents and consequences of medical technology. Two active learning strategies were used: (a) PBL to explore the historical basis of procedures used to diagnose, prevent and treat a single disease, tuberculosis, and (b) a concurrent inquiry-based component that permitted individual exploration of other medical technologies and demonstration of learning through diverse options (book reviews, conversations, essays, archival research, oral exams). This course was highly rated by students with an overall rating of 9.5 ± 0.7 (36 students from 2008–2012).

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.313
Teacher spread0.291 · 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