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Record W3027508590 · doi:10.1097/nne.0000000000000850

Rewriting the Microaggression Narrative

2020· article· en· W3027508590 on OpenAlexaff
Brigit Carter, Jacquelyn McMillian‐Bohler

Bibliographic record

VenueNurse Educator · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsEthnic groupDiversity (politics)PsychologyNarrativeRacial biasRace (biology)Prejudice (legal term)Cultural diversityMedical educationNursingSocial psychologyMedicineGender studiesSociologyLinguistics

Abstract

fetched live from OpenAlex

BACKGROUND: It is recognized that expanding the number of racial/ethnic minority nurses is key to addressing the challenges of health disparities. However, some schools of nursing have not typically experienced diversity. PROBLEM: Diverse nursing students experience increasingly high rates of exposure to microaggression, discrimination, and bias in the clinical and classroom settings. Providing nursing students with strategies to respond to microaggressions can reduce barriers to nursing education. APPROACH: An interactive workshop based on the Theater of the Oppressed performance technique was developed to increase students' ability to recognize/respond to microaggressions. OUTCOMES: Students (n = 97) completed a preworkshop-postworkshop evaluation. After participation, students indicated an improved ability to recognize microaggressions with intent to respond when they occur. CONCLUSIONS: Race was the most common microaggression addressed in the skits, followed by gender and ability. The interactive nature of the workshop allowed students to practice strategies to address microaggressions.

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)0.002
metaresearch head score (Gemma)0.006
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: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.011
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.052
GPT teacher head0.388
Teacher spread0.336 · 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
GenreCommentary

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

Citations20
Published2020
Admission routes1
Has abstractyes

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