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Record W3088035547 · doi:10.1002/aet2.10533

Multisource Feedback in the Trauma Context: Priorities and Perspectives

2020· article· en· W3088035547 on OpenAlexaff
Andrei Garcia Popov, Andrew K. Hall, Timothy Chaplin

Bibliographic record

VenueAEM Education and Training · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsCompetence (human resources)Situation awarenessPsychologyContext (archaeology)Situational ethicsMultidisciplinary approachMedical educationApplied psychologyMedicineSocial psychologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES: Trauma resuscitations require competence in both clinical and nonclinical skills but these can be difficult to observe and assess. Multisource feedback (MSF) is workplace-based, involves the direct observation of learners, and can provide feedback on nonclinical skills. We sought to compare and contrast the priorities of multidisciplinary trauma team members when assessing resident trauma team captain (TTC) performance. Additionally, we aimed to describe the nature of the assessment and perceived the utility of incorporating MSF into the trauma context. METHODS: A convenience sample of 10 trauma team activations were observed. Following each activation, the attending physician trauma team leader (TTL), TTC, and a registered nurse (RN) participated in a semistructured interview. MSF was not provided to the TTC for the purpose of this study because MSF was not part of the assessment process of TTCs at the time of this study and maintaining anonymity may have encouraged more honest interview responses. Transcripts from each assessor group (TTL, TTC, RN) were coded and assigned to one of the five crisis resource management skills: leadership, communication, situational awareness, resource utilization, and problem-solving. Comments were also coded as positive, negative, or neutral as interpreted by the coder. RESULTS: All assessor groups mentioned communication skills most frequently. After communication, the RN and TTC groups commented on situational awareness most frequently, comprising 15 and 29% of their total responses, respectively, whereas 31% of the TTL comments focused on leadership skills. The RN and TTL groups provided positive assessments, with 51 and 42% of their respective comments coded as positive. Forty-five percent of self-assessment comments in the TTC group were negative. All (100%) of the TTC and TTL respondents felt that incorporating MSF would add to the quality of feedback, only 66% of the RN group felt that way. CONCLUSIONS: We found that each assessor group brings a unique focus and perspective to the assessment of resident TTC performance. The future inclusion of MSF in the trauma team context has the potential to enhance the learning environment in a clinical arena that is difficult to directly observe and assess.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.123
GPT teacher head0.361
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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