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Record W4402595367 · doi:10.3928/01484834-20240612-04

Simulation Experiences Focusing on Addressing Culturally Based Hidden Bias and RacisM (A-CHARM)

2024· article· en· W4402595367 on OpenAlexaff
Jenny S. Li, Michaela Patterson, Nathaniel Gumapac, Crystal Sau, Sumaya Mohamed, Hayan Amina Yusef, Monakshi Sawhney

Bibliographic record

VenueJournal of Nursing Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsQueen's University
Fundersnot available
KeywordsCharm (quantum number)RacismRacial biasPsychologySociologyGender studiesParticle physicsPhysics

Abstract

fetched live from OpenAlex

Background Racism experienced by nursing students contributes to a loss of confidence and anxiety. The Addressing Culturally Based Hidden Bias and RacisM (A-CHARM) nursing project simulation experiences (SEs) provide opportunities to practice addressing racism/inappropriate comments experienced in the clinical setting. The aim is to describe the development of the A-CHARM nursing project SEs. Method The frameworks used in the development of the SEs include the (1) six-step approach, (2) ERASE framework, (3) SENSE debriefing model, and (4) Microaggressions Triangle model. Results Five SEs were created that depicted scenes where a nursing student encounters racism/inappropriate comments. Each SE aligned with one of the frameworks and users utilized the framework to navigate the SE. Conclusion The A-CHARM nursing SEs may benefit nursing students by enhancing their knowledge and skills related to racism or inappropriate comments in clinical settings. Future research will evaluate the impact of the SEs on nursing students and clinical faculty. [ J Nurs Educ . 2025;64(7):449–453.]

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)0.001
metaresearch head score (Gemma)0.001
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 score0.597
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.199
GPT teacher head0.488
Teacher spread0.289 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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