MétaCan
Menu
Back to cohort
Record W2136021688 · doi:10.1080/01421590701770454

A pilot study designed to acquaint medical educators with basic pedagogic principles

2008· article· en· W2136021688 on OpenAlexaff
Peter J. McLeod, James R. Brawer, Yvonne Steinert, Colin Chalk, Audrey McLeod

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedical educationMathematics educationPsychologyMedicineEngineering ethicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Faculty development activities in medical schools regularly target teaching behaviours but rarely address basic pedagogic principles underlying those behaviours. Although many teachers have an intuitive or tacit knowledge of basic pedagogic principles, overt knowledge of fundamental educational principles is rare. AIMS: We conducted a short-term pilot study designed to transform teachers' tacit knowledge into explicit knowledge of pedagogic principles. We hypothesized that conscious awareness of these principles will positively influence their teaching effectiveness. METHODS: The intervention included a workshop, provision of a workbook on pedagogic principles and free access to educational consultants. For the intervention, we chose a purposive sample of experienced teachers at our medical school. RESULTS AND CONCLUSIONS: Evaluation of the impact of the intervention using questionnaires and semi-structured interviews revealed three notable findings; 1. Participants were surprised to discover the existence of an extensive body of pedagogic science underlying teaching and learning. 2. They were enthusiastic about the intervention and expressed interest in learning more about basic pedagogic principles. 3. The knowledge acquired had an immediate impact on their teaching.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.001

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 head0.097
GPT teacher head0.381
Teacher spread0.284 · 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 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

Citations18
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueMedical TeacherSame topicInnovations in Medical EducationFrench-language works237,207