Racism and Nursing Leadership in Massachusetts
Bibliographic record
Abstract
BACKGROUND: Nursing in the United States has evolved within the same historical context that has reproduced and spread racism worldwide. Nurse administrators are integral to the quality of nurses' practice and play a key role in eliminating racial injustice in places of work. PURPOSE: Using a feminist and critical race feminist framework, this study examined Massachusetts nurses' experiences of racism in their places of work, focusing on nurse administrators' influence on the nonadministrator (staff nurse) experience of racism experiences before and after George Floyd's death. METHODS: An investigator-developed, electronic survey was sent to Massachusetts professional nursing organizations for distribution to their members in 2021. Two hundred nineteen nurse respondents completed Likert-scale and open-ended branching logic survey questions to yield the quantitative and qualitative data analyzed for this mixed-methods study. FINDINGS: Nurse administrators were: 1) more likely than staff nurses to state that policies and meetings to address racism and diversity, equity, and inclusion had taken place before and after George Floyd's murder; and 2) less likely than staff nurses to directly experience racism at the hands of a colleague or a superior. Nurse administrators influence staff nurses' experiences of racism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".