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Record W2104283430 · doi:10.3109/0142159x.2015.1006599

Using patients’ charts to assess medical trainees in the workplace: A systematic review

2015· review· en· W2104283430 on OpenAlexaff
Heidi Al‐Wassia, Rolina Al‐Wassia, Shadi Shihata, Yoon Soo Park, Ara Tekian

Bibliographic record

VenueMedical Teacher · 2015
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsycINFOCINAHLMEDLINEObservational studyChartMedical educationMedicineRecallReliability (semiconductor)PsychologyNursingPsychological intervention

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this review is to summarize and critically appraise existing evidence on the use of chart stimulated recall (CSR) and case-based discussion (CBD) as an assessment tool for medical trainees. METHODS: Medline, Embase, CINAHL, PsycINFO, Educational Resources Information Centre (ERIC), Web of Science, and the Cochrane Central Register of Controlled Trials were searched for original articles on the use of CSR or CBD as an assessment method for trainees in all medical specialties. RESULTS: Four qualitative and three observational non-comparative studies were eligible for this review. The number of patient-chart encounters needed to achieve sufficient reliability varied across studies. None of the included studies evaluated the content validity of the tool. Both trainees and assessors expressed high level of satisfaction with the tool; however, inadequate training, different interpretation of the scoring scales and skills needed to give feedback were addressed as limitations for conducting the assessment. CONCLUSION: There is still no compelling evidence for the use of patient's chart to evaluate medical trainees in the workplace. A body of evidence that is valid, reliable, and documents the educational effect in support of the use of patients' charts to assess medical trainees is needed.

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)0.017
metaresearch head score (Gemma)0.069
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.069
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.239
GPT teacher head0.494
Teacher spread0.255 · 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 designSystematic review
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

Citations12
Published2015
Admission routes1
Has abstractyes

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