Understanding Nurses' Needs Regarding Tailored, Evidence‐Based Sleep Education and Training
Bibliographic record
Abstract
AIM: Identify desired training content for shift-working nurses to improve their sleep and fatigue. DESIGN: A descriptive qualitative design. METHODS: We recruited night shift nurses (N = 23) to provide feedback during virtual focus groups/interviews. Data collection occurred in the U.S. between March and June 2024. Participants were presented with sleep and fatigue topics derived from the literature. Focus group/interview data were collected and transcribed. Data were analysed using a hybrid deductive-inductive manifest content analysis with an a priori coding schema based on topics shared during data collection. Data not fitting the schema, yet informing content, were analysed inductively. RESULTS: Three themes aligned with literature-derived topics. Theme 1, Why We Sleep and Why Should Nurses Care, explains the importance of sleep to health. Theme 2, Sleep Practices for Nurses to Support Health and Social Relationships, describes healthy strategies to promote sleep for enhanced quality of life. Theme 3, Fatigue and Work, illustrates the significance of nurse sleep and fatigue risk mitigation to safe working conditions and patient care. CONCLUSIONS: Study findings highlight night shift nurses' interest in gaining evidence-based information to promote their sleep. Sleep education and training could fill a knowledge and skills gap, not often offered in school or workplace. IMPLICATIONS FOR THE PROFESSION: Identifying themes relevant to nurses may help increase the development and availability of sleep education and training currently tailored for nurses. IMPACT: Study findings describe content night shift nurses' desire for sleep and fatigue training, serving as an important first step in developing programmes most relevant to shift-working nurses. Our analysis found the findings largely align with key components workers should receive in sleep education and training and reinforced the need for employers to offer such training. This study could benefit the nursing workforce and employers who expect rested, high-functioning nurses to care for patients. REPORTING METHOD: Standards for Reporting Qualitative Research. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL AND PROTOCOL REGISTRATION: Clinicaltrials.gov, NCT06105307.
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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 | 0.000 | 0.000 |
| 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".