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Record W2534560955 · doi:10.22374/cjgim.v11i3.148

An Ambulatory Clinical Teaching Unit: Filling the Outpatient Gap in Internal Medicine Residency Training

2016· article· en· W2534560955 on OpenAlexvenueaboutno aff
Ali Kara, Akbar Panju, M Fulford

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubspecialtyAmbulatoryCore competencyCurriculumMultidisciplinary approachAmbulatory careMedical educationFamily medicineInternal medicinePsychologyHealth careManagementPedagogy

Abstract

fetched live from OpenAlex

The majority of time in a core General Internal Medicine (GIM) residency is spent focusing on inpatient medicine, with relatively little time devoted to ambulatory medicine. The Royal College of Physicians and Surgeons of Canada has mandated an improvement in ambulatory exposure. Unfortunately, most ambulatory experiences tend to lack formal structure, a dedicated educational curriculum, and graduated learner-specific responsibilities. The recent Royal College recognition of GIM as a subspecialty places renewed emphasis on core IM training providing a more comprehensive exposure to outpatient medicine as management of patients with multiple complex conditions may be best managed by a general internist. In July 2015, McMaster University opened an outpatient medicine clinic which is designed to be an Ambulatory Clinical Teaching Unit (A-CTU). The A-CTU provides a structured clinical environment which is focused on the management of medically-complex patients. It uses a multidisciplinary model, graded learner levels of responsibility and a dedicated educational curriculum. The unique structure of the A-CTU allows for the assessment of milestones and EP As (entrustable professional activities) pertaining to consultation skills and chronic disease management, in keeping with competence by design.

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)0.009
metaresearch head score (Gemma)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.232
GPT teacher head0.499
Teacher spread0.267 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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