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Record W2025405253 · doi:10.1080/10401330802384177

Assessing Change in Clinical Teaching Skills: Are We Up for the Challenge?

2008· article· en· W2025405253 on OpenAlexaff
Meridith B. Marks, Timothy J. Wood, Janet Nuth, Claire Touchie, Heather V. O’Brien, Alison Dugan

Bibliographic record

VenueTeaching and Learning in Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMedical Council of CanadaUniversity of Ottawa
Fundersnot available
KeywordsPsychological interventionMedical educationPsychologyTest (biology)Scale (ratio)MedicineClinical psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The faculty development community has been challenged to more rigorously assess program impact and move beyond traditional outcomes of knowledge tests and self ratings. PURPOSE: The purpose was to (a) assess our ability to measure supervisors' feedback skills as demonstrated in a clinical setting and (b) compare the results with traditional outcome measures of faculty development interventions. METHODS: A pre-post study design was used. Resident and expert ratings of supervisors' demonstrated feedback skills were compared with traditional outcomes, including a knowledge test and participant self-evaluation. RESULTS: Pre-post knowledge increased significantly (pre = 61%, post = 85%; p < .001) as did participant's self-evaluation scores (pre = 4.13, post = 4.79; p < .001). Participants' self-evaluations were moderately to poorly correlated with resident (pre r = .20, post r = .08) and expert ratings (pre r = .43, post r = -.52). Residents and experts would need to evaluate 110 and 200 participants, respectively, to reach significance. CONCLUSIONS: It is possible to measure feedback skills in a clinical setting. Although traditional outcome measures show a significant effect, demonstrating change in teaching behaviors used in practice will require larger scale studies than typically undertaken currently.

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)0.012
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.162
GPT teacher head0.478
Teacher spread0.316 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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