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Record W1993305829 · doi:10.1186/1472-6920-11-16

Who is teaching and supervising our junior residents' central venous catheterizations?

2011· article· en· W1993305829 on OpenAlexaffabout
Irene Ma, Elise Teteris, James M. Roberts, Maria Bacchus

Bibliographic record

VenueBMC Medical Education · 2011
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsCurriculumMedicineMedical educationBaseline (sea)PsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: The extent to which medical residents are involved in the teaching and supervision of medical procedures is unknown. This study aims to evaluate the teaching and supervision of junior residents in central venous catheterization (CVC) by resident-teachers. METHODS: All PGY-1 internal medicine residents at two Canadian academic institutions were invited to complete a survey on their CVC experience, teaching, and supervision prior to their enrolment in a simulator CVC training curriculum. RESULTS: Of the 69 eligible PGY-1 residents, 32 (46%) consenting participants were included in the study. There were no significant baseline differences between participants from the two institutions in terms of sex, number of ICU months completed, previous CVC training received, number of CVCs observed and performed. Only 16 participants (50%) received any CVC training at baseline. Of those who received any training, 63% were taught only by senior resident-teachers. A total of 81 CVCs were placed by 17 participants. Thirty-two CVCs (45%) were supervised by resident-teachers. CONCLUSIONS: Resident-teachers play a significant role both in the teaching and supervision of CVCs placed by junior residents. Educational efforts should focus on preparing residents for their role in teaching and supervision of procedures.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.376
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2011
Admission routes2
Has abstractyes

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