MétaCan
Menu
Back to cohort

Reducing Reliance on Hospitalized Patients for Undergraduate Clinical Skills Teaching in Internal Medicine

2000· article· en· W2021878891 on OpenAlexaff
Donald Farquhar

Bibliographic record

VenueAcademic Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsTUTORPhysical examinationObjective structured clinical examinationMedicinePopulationMedical historyMedical educationHealth careFamily medicinePsychologySurgeryPedagogy

Abstract

fetched live from OpenAlex

Objective: Teaching clinical skills to undergraduates in internal medicine traditionally has relied on an accessible pool of hospitalized patients. Changes in health care delivery, however, have challenged this model significantly: Reductions in hospital beds and compressed lengths of stay have created a smaller, highly selected population of inpatients whose severity of illness and frailty often make them unsuitable for encounters with medical students. We designed and pilot-tested a model of clinical skills teaching that reduces reliance on hospitalized patients by using a combination of ambulatory clinic encounters and inpatient case scenarios. Description: At Queen's University, the undergraduate clinical skills program spans each of the first three years. While the first and second years emphasize communication skills, history taking, and physical examination, the goal of the third year is to provide a consolidative experience with greater emphasis on applying clinical and basic science knowledge to the data they collect through history taking and physical examination. In the third year, students meet in groups of four with a faculty tutor on two half-days per week for six consecutive weeks. In the traditional model, students interview and examine an inpatient on the first half-day and present their findings on the second half-day. In the new model, two students attend an internal medicine consultation clinic on the first half-day to see a newly referred patient and then, with the tutor, formulate a plan of management and follow-up. The remaining two students are given an inpatient case scenario outlining a patient's presenting history, physical findings, and laboratory data and are asked to prepare a written assessment of the patient's problem(s) in the same format that would be required in a hospital admission note, including a set of admission orders. The scenarios are selected so as to provide a sampling of the most common acute medical problems requiring hospitalization. On the second half-day, the students present their ambulatory cases and inpatient scenarios to the group. Assignments alternate biweekly, so that over the six-week course each student attends three clinics and analyzes three case scenarios. Direct faculty observation of the student's performance of the history and physical examination is mandatory in the first ambulatory encounter and optional in subsequent ones. Discussion: Pilot-testing of the new model has yielded very positive feedback. Strengths of the ambulatory component, as cited by the students, include the breadth of clinical problems, the opportunity for immediate feedback from the tutor, and the sense of contributing to patient care in “real time.” Moreover, the students feel that the inpatient scenarios provide a practical opportunity to prepare for their upcoming clerkship in medicine. Challenges to the new model include the constraints on ambulatory clinic space and the additional time commitment required by faculty tutors. We plan to expand this program in 2000–01 to include a greater proportion of the third-year class, and to undertake a formal evaluation in comparison with the traditional approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.029
GPT teacher head0.425
Teacher spread0.396 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueAcademic MedicineSame topicInnovations in Medical EducationFrench-language works237,207