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Teaching skills for medical residents: are these important? A narrative review of the literature

2018· review· en· W2922195661 on OpenAlexaboutno aff
Saadallah Azor Fakhouri Filho, Lorena Pinho Feijó, Kristopherson Lustosa Augusto, Maria do Patrocínio Tenório Nunes

Bibliographic record

VenueSao Paulo Medical Journal · 2018
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNarrativeMedical educationNarrative reviewIntensive care medicineLiterature

Abstract

fetched live from OpenAlex

BACKGROUND: There is extensive evidence, mainly from the United States and Canada, that points towards the need to train medical residents in teaching skills. Much of the "informal curriculum", including professional values, is taught by residents when consultants are not around. Furthermore, data from the 1960s show the importance of acquiring these skills, not only for residents but also for all doctors. -Teaching moments can be identified in simple daily situations, like discussing a clinical situation with patients and their families, planning patients' care with the healthcare team or teaching peers and medical students. The aim here was to examine the significance of resident teaching courses and estimate the effectiveness of these courses and the state of the art in Brazil. METHODS: We conducted a review of the literature, using the MEDLINE, PubMed, SciELO and LILACS databases to extract relevant articles describing residents-as-teachers (RaT) programs and the importance of teaching skills for medical residents. This review formed part of the development of a doctoral project on medical education. RESULTS: Original articles, reviews and systematic reviews were used to produce this paper as part of a doctoral project. CONCLUSIONS: RaT programs are important in clinical practice and as role models for junior learners. -Moreover, these educational programs improve residents' self-assessed teaching behaviors and teaching confidence. On the other hand, RaT program curricula are limited by both the number of studies and their methodologies. In Brazil, there is no such experience, according to the data gathered here, except for one master's thesis.

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)0.014
metaresearch head score (Gemma)0.105
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.580
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.105
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0020.007
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 head0.020
GPT teacher head0.424
Teacher spread0.403 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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