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Evaluation of a Teaching Workshop for Residents at the University of Saskatchewan: A Pilot Study

2004· article· en· W37701703 on OpenAlexaffabout
Marcel D’Eon

Bibliographic record

VenueAcademic Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIntervention (counseling)Medical educationPsychologySet (abstract data type)MedicineNursingComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The University of Saskatchewan College of Medicine in Saskatoon, Canada, has been running a two-day workshop for teachers since 1993 to which both faculty and residents had been invited. Although the design of the workshop is consistent with principles of adult learning, it was important to determine if the workshop effectively helped residents to acquire and then use key teaching skills in real-world situations where they were called on to organize and make presentations. METHOD: This study, conducted in 1998 and 1999 with residents only, used a randomized controlled experiment with third-party ratings of before and after videotaped teaching sessions done in actual performance settings. There were eight residents each in the control and intervention groups. RESULTS: The intervention group made statistically significant and positive changes in two key areas taught in the workshop and showed slight improvement in a third. The changes made by the residents in the intervention group were in presenting the opening "set" (41.4% absolute improvement) and the use of instructional objectives (11.5%). The "body" of their teaching sessions increased slightly (9.3%). The control group held relatively stable. CONCLUSION: While this study demonstrates that the workshop likely made a difference in the teaching performance of the intervention group, the small sample size (eight in each group) and the presence of confounding variables suggest that further research should be conducted.

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)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.100
GPT teacher head0.410
Teacher spread0.310 · 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 designQualitative
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

Citations34
Published2004
Admission routes2
Has abstractyes

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