MétaCan
Menu
Back to cohort
Record W1862187075 · doi:10.3109/0142159x.2015.1031733

Progress testing in family medicine – A novel use for simulated office oral exams

2015· article· en· W1862187075 on OpenAlexaffabout
Kendall Noel, Douglas Archibald, Carlos Brailovsky, Ashley Mautbur

Bibliographic record

VenueMedical Teacher · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsCollege of Family Physicians of CanadaUniversity of Ottawa
Fundersnot available
KeywordsMedicineTest (biology)Clinical PracticeFamily medicineCertificationRepeated measures designRisk assessmentMedical educationStatisticsComputer scienceMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Simulated office orals (SOOs) are used by the College of Family Physicians of Canada as a method to evaluate family medicine resident readiness for clinical practice. The use of SOOs as a progress test would provide residency programs with useful information to determine resident readiness for challenging the certification exam. The data from a progress test could then be easily manipulated to generate a risk assessment plot. METHODS: During a prospective cohort study conducted at the University of Ottawa, the feasibility of using practice SOO sessions, a structured clinical exam, as a progress test was explored. Twenty-three residents participated in all four practice SOO sessions and their results were entered into a risk assessment plot. RESULTS: Repeated measures analysis of the data using ANOVA demonstrated that the residents' scores at each time point were statistically different from each other, generating an F(3, 66) = 27.52, p < 0.001, η(2) = 0.55.and that the relationship over time was linear with an F(1, 22) = 123.80, p < 0.001, η(2) = 0.85. At the final time point, a risk assessment resulted in no learners mapping to quadrants III or IV. CONCLUSIONS: Our results demonstrate the feasibility of utilizing a SOO exam, a clinical exam, as a progress test. In addition, we propose generating a risk assessment plot, using the data from the Fall 2013 and Spring 2014 practice SOO sessions, as a means of identifying residents at risk. Further studies will be needed to confirm the utility of this analysis. Combined with other measures acquired during in-training evaluation, the utilization of practice SOOs as a progress test will provide program directors with valuable information on resident progression.

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)0.003
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.279
GPT teacher head0.438
Teacher spread0.160 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

Explore more

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