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Record W4310690250 · doi:10.22374/cjgim.v17i4.635

Direct Observation and Feedback on the Internal Medicine Clinical Teaching Unit

2022· article· en· W4310690250 on OpenAlexafffundvenue
Michael Ke Wang, Daniel Brandt Vegas

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsImpactMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
KeywordsObservational studyMedical educationMedicineUnit (ring theory)HumanitiesPsychologyMathematics educationInternal medicineArt

Abstract

fetched live from OpenAlex

Background: Direct observation is an invaluable tool for assessing clinical skills. However, it is unclear whether trainees are regularly observed on internal medicine clinical teaching units (CTUs). Methods: A web-based survey was distributed to medical students and residents completing rotations on inpatient internal medicine CTUs. Participants recorded the frequency of direct observation and observational feedback received over the past week. Results: Of the 189 survey respondents, 76% reported receiving direct observation at least once. On average, six skill-specific observations were reported by each learner, with an average of two different skills being observed. Bedside clinical decision-making and physical examination skills were observed most frequently. Feedback was least often provided after the direct observation of physical examination and communication skills. Conclusions: A quarter of trainees were not regularly observed at the internal medicine CTUs. The optimal frequency of direct observation requires further 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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.383
Teacher spread0.292 · 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 designNot applicable
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

Citations2
Published2022
Admission routes3
Has abstractyes

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