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Tracking Clerkship Experience: Just What Do Clinical Clerks See on an Elder Care Rotation?

2007· article· en· W1606198578 on OpenAlexaffabout
Laura L. Diachun, Lisa Van Bussel, Andrea Ens, Loretta M. Hillier

Bibliographic record

VenueJournal of the American Geriatrics Society · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicineCurriculumDeliriumMedical educationClinical clerkshipTracking (education)CognitionGeriatricsMEDLINENursingPsychologyPsychiatryPedagogy

Abstract

fetched live from OpenAlex

The Schulich School of Medicine, University of Western Ontario, Canada, has created a mandatory clerkship in Elder Care that consists of small group seminars, clinical experiences, and an Elder Care manual. This article describes the use of a paper-based log to track students' clinical encounters to determine whether the Elder Care clerkship offers students the opportunity for a broad range of clinical experiences to address curriculum objectives. Using a paper-based log that was completed after each clinical encounter, students recorded information including the reason for assessment, tests completed, care recommendations, and personal reflections. Each of 70 students completed an average of 5.5 logs. Cognitive/psychiatric, medical, functional, and social problems were reported in more than 83% of the logs. Almost all students saw at least one patient with cognitive decline and one with depressive symptoms. Only six students reported seeing a patient with delirium. Students were able to think reflectively on their experiences. In matching the clerkship objectives to the learning modality(ies) in which they were addressed, it was found that knowledge-related objectives were supported primarily by seminars and manual content. Skills-related objectives were supported primarily by clinical experiences. The clinical experience logs used in this study provided evidence that, in the Elder Care clerkship, for the most part, students are seeing what we think they should be seeing. Study results have informed the revision of the logs, which will be an ongoing method of tracking objectives and students' reflections and ensuring continuous quality improvement.

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)0.006
metaresearch head score (Gemma)0.041
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.074
GPT teacher head0.486
Teacher spread0.413 · 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

Citations3
Published2007
Admission routes2
Has abstractyes

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