Sleep inequities in nursing: A descriptive qualitative study on causes of poor sleep among black nurses in the United States
Bibliographic record
Abstract
Background: Sleep is critical to general health and occupational safety of workers. Black nurses in the United States report sleeping less than their White counterparts, indicating sleep inequity exists. Understanding what workplace factors contributing to this inequity and suggestions for improvement are vital to protecting nurses.Methods: A descriptive qualitative research design with content analysis was used to examine focus group data from Black nurses working in the United States. Participants were invited to virtual focus groups or interviews to answer questions about their sleep. Questions were guided by the Social Ecological Model for Sleep.Results: Fifteen nurses participated. Four themes emerged: Societal Impact, Workplace, Interpersonal-Cultural Context, and Individual. Twelve sub-themes were identified that described factors that affect all nurses (i.e., night shift, long work hours) versus societal and interpersonal events tied to racism that are most impactful for Black nurses’ sleep. Participants offered six suggestions for changing the healthcare setting to increase a sense of belonging.Conclusions: To improve sleep equity among Black nurses working in healthcare settings, a holistic approach towards worker health and safety may help attenuate individual risks from poor sleep. Systemic organizational efforts to increase belonging among staff could benefit from fostering trusting relationships with Black nurses, as well as increasing the diversity of healthcare leaders and managers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".