MétaCan
Menu
Back to cohort
Record W2140278466 · doi:10.1080/01421590802335900

Which pedagogical principles should clinical teachers know? Teachers and education experts disagree Disagreement on important pedagogical principles

2009· article· en· W2140278466 on OpenAlexaff
Peter J. McLeod, Yvonne Steinert, Colin Chalk, Richard L. Cruess, Sylvia R. Cruess, Sarkis Meterissian, Saleem Razack, Linda Snell

Bibliographic record

VenueMedical Teacher · 2009
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsPsychologyMathematics educationMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: In a previous study, a group of non-clinician medical education experts identified 30 pedagogical principles, knowledge of which might enhance clinical teaching effectiveness. AIMS: To assess expert teachers? perceptions of which basic pedagogical principles, if known and understood, would enhance their teaching effectiveness. METHOD: We conducted an on-line Delphi consensus-building study with 25 expert clinical teachers who rated the importance to teaching effectiveness of each of the 30 principles. RESULTS: There was agreement between clinicians and PhD education experts on the importance of several of the principles but there was major disagreement between the 2 groups for many principles, including those related to assessment and those relevant to clinical teachers? day to day teaching activities. CONCLUSIONS: The lack of concordance between clinical teachers and education experts with respect to how the 30 principles rank in importance may have serious implications for faculty development and for the design, development, and assessment of educational programs. Program directors and curriculum designers should exploit the strengths of both clinician and non-clinician educators to assure the success of educational programs.

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)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.223
GPT teacher head0.476
Teacher spread0.253 · 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

Citations38
Published2009
Admission routes1
Has abstractyes

Explore more

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