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Record W3176971527 · doi:10.1097/jnc.0000000000000270

A Review of Nursing Position Statements on Racism Following the Murder of George Floyd and Other Black Americans

2021· review· en· W3176971527 on OpenAlexaff
Amelia Knopf, Henna Budhwani, Carmen H. Logie, Ukamaka M. Oruche, Erin Wyatt, Claire Burke Draucker

Bibliographic record

VenueJournal of the Association of Nurses in AIDS Care · 2021
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsPublic Health Ontario
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Mental HealthUniversity of Michigan
KeywordsGeorge (robot)RacismCriminologyPosition (finance)PsychologyNursingMedicineSociologyGender studiesHistoryArt historyBusiness

Abstract

fetched live from OpenAlex

ABSTRACT: National outrage over the killings of George Floyd and other Black Americans in the United States prompted public outcry against police brutality and racism in law enforcement and drew national attention to systemic racism as a public health crisis. In response, during the summer of 2020 many health organizations issued position statements in response to the murders. This article examines such statements issued by 3 prominent nursing organizations and 18 schools of nursing. Thematic analysis revealed six themes in the statements of the professional organizations, and a content analysis revealed that the statements of the schools of nursing were generally aligned with these themes. Such position statements can provide a viable approach to the public commitment to anti-racist reforms, but it is unclear if such statements can promote meaningful and measurable change.

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)0.004
metaresearch head score (Gemma)0.015
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.469
Teacher spread0.418 · 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
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

Citations27
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Association of Nurses in AIDS CareSame topicMigration, Health and TraumaFrench-language works237,207