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Record W2023930596 · doi:10.1207/s15328015tlm1701_8

The Influence of Residents Training Level on Their Evaluation of Clinical Teaching Faculty

2005· article· en· W2023930596 on OpenAlexaff
Ivan P. Steiner, Philip W. Yoon, Karen D. Kelly, Barry Diner, Sandra Blitz, Michel Donoff, Brian H. Rowe

Bibliographic record

VenueTeaching and Learning in Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Alberta HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineMedical educationAssociation (psychology)Family medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Evaluations by learners are the most common sources of information on teaching. There is some debate about the role of these assessments, but the overall evaluation of faculty by learners was found to be valid and reliable. PURPOSE: The purpose of this study was to examine the relationship between the level of training of family medicine residents and their evaluation of emergency medicine clinical teachers over time. METHODS: A prospective cohort analysis of 6 years of faculty evaluation of 115 teachers was conducted. RESULTS: The 562 residents returned 3,046 valid individual evaluations. There was no significant association between the level of residents' training and the ratings for clinical instruction (p > .05). Resident evaluations did not vary by time of year (p > .05); however, they did significantly differ by year of evaluation, showing that ratings increased over the 6 years of the study (p < .0001). CONCLUSIONS: Neither the residents' level of training nor the timing during the academic year were significant independent predictors of perceived superior teaching performance, although ratings increased over the 6 years of the study.

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)0.044
metaresearch head score (Gemma)0.075
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.075
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 integrity0.0000.003
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 head0.222
GPT teacher head0.505
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2005
Admission routes1
Has abstractyes

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