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Developing Case-specific Checklists for Standardized-patient—Based Assessments in Internal Medicine

2000· review· en· W1993902198 on OpenAlexaboutno aff
Simone Gorter, Jan‐Joost Rethans, Albert J.J.A. Scherpbier, Désirée van der Heijde, Harry Houben, Cees van der Vleuten, Sjef van der Linden

Bibliographic record

VenueAcademic Medicine · 2000
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistMEDLINEMedical educationMedicinePsychologyMedical physicsFamily medicine

Abstract

fetched live from OpenAlex

PURPOSE: To review the literature on the methods used in writing case-specific checklists for studies of internal medicine physicians' performances that were assessed by standardized patients. METHOD: The authors searched Medline, Embase, Psychlit, and ERIC for articles in English published between 1966 and February 1998. The following search string was used: "[(standardi(*) or simulat(*) or programm(*)) near (patient(*) or client(*) or consultati(*))] and internal medicine." The authors then searched the reference lists of papers retrieved from the database searches, as well as those from seven proceedings of the International Ottawa Conference on Medical Education and Assessment. RESULTS: The procedure yielded 29 relevant articles: database searches yielded 14 published reports dealing with case-specific checklists, 11 articles were culled from the reference lists of these papers, and the Ottawa Conference proceedings yielded four articles. Only 12 articles reported specifically on the development of checklists. In general, there were three sources used for developing checklists: panels of experts, the investigators themselves, and responses from expert physicians to written protocols. No article indicated that researchers had relied exclusively on data from the literature to compose their checklists. Only three articles indicated that literature sources had informed their checklist development. All articles except one relied on explicit criteria for the inclusion of items on the checklists. In 21 of the 29 articles, the checklists had been scored by SPs, but the scoring of specific items on the checklists varied according to the purpose of the SP-physician encounter. Only four of the articles made the checklists available or indicated that the checklists could be obtained from the authors. CONCLUSION: The development of case-specific checklists for SP examinations of physicians' performance has received little attention. To judge the validity of studies of physicians' performances that use SPs, the development processes for the checklists need to be more fully described to enable readers to evaluate the validity and reliability of the studies.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.159
GPT teacher head0.504
Teacher spread0.345 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations71
Published2000
Admission routes1
Has abstractyes

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