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Record W2762092629 · doi:10.1093/pch/19.6.e35-134

137: Using SBAR (Situation, Background, Assessment and Recommendations) to Improve Resident Communication

2014· article· en· W2762092629 on OpenAlexaffabout
D Wang, Elham Sabri, Kristina Krmpotic, A.-T. Lobos

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsAgricultural Research Institute of Ontario
Fundersnot available
KeywordsChecklistMedicineSession (web analytics)MnemonicLikert scaleMedical educationPsychologyComputer science

Abstract

fetched live from OpenAlex

It is important for residents to communicate effectively with attending physicians, especially in critical on call situations. In recent years, handover mnemonics have gained attention as a way to improve communication. Although one of the best studied is SBAR (situation, background, assessment and recommendations), few publications have examined the effect of formal SBAR training and usage on resident to staff communication. To determine if a structured SBAR teaching session and an SBAR based communication checklist improves resident self-assessment of their ability to communicate in critical situations. A convenience sample of 22 paediatric residents at the Children's Hospital of Eastern Ontario was recruited to participate in the study. Participants watched a video of a mock-code scenario and dictated an audio summary of the case, as though they were presenting to an attending physician. After baseline recordings, participants were randomly divided into three groups (stratified by year of training): SBAR teaching and checklist (group 1), SBAR teaching only (group 2), and no teaching or checklist (control group). Teaching consisted of a one-hour didactic session on SBAR. Participants then watched another mock-code scenario and recorded a second summary. After each recording, participants completed self-assessments using Likert-scales. Ultimately, there were nine residents in group 1, four residents in group 2 and nine residents in the control group. The year of training was evenly distributed within each group (ranging from year 1 to 3). Of the 13 residents who received SBAR teaching, 92% agreed that it was helpful. All nine residents who used the checklist felt that it was helpful. Residents in group 1 assessed themselves as being more organized in their presentation compared to baseline (P=0.03) which is also significant when compared to the change in the control group (P=0.02). Additionally there was a perceived improvement in their ability to handover all necessary information (P<0.01) but this was not significant when compared to the change in the control group (P=0.7). There was no significant difference in the change from baseline for either category of self-assessment when group 2 and the control group were compared. Formal SBAR teaching and use of an SBAR checklist were helpful in improving resident communication, as indicated by improved resident self-assessment of presentation organization from baseline compared to those with no teaching and no checklist. The effect of teaching alone was difficult to interpret due to a small sample size. Subsequent analyses will include blinded-staff evaluation of the recorded communications for content and organization.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.003

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 head0.060
GPT teacher head0.451
Teacher spread0.391 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2014
Admission routes2
Has abstractyes

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