Reach of an Occupational Health and Safety Program to Improve Sleep and Fatigue Among Nurses
Bibliographic record
Abstract
BACKGROUND: Training and education may benefit nurses whose nonstandard work hours put them at risk of poor sleep, fatigue, and ensuing adverse health and safety outcomes. The National Institute for Occupational Safety and Health (NIOSH) published "Training for Nurses on Shift Work and Long Work Hours" in 2015 as a free online resource which remains one of the few trainings available on this topic. However, the extent to which nurses have completed the program and the characteristics of current learners have not been examined. OBJECTIVE: We aimed to describe the potential reach of the NIOSH Training for Nurses between May 2015 through December 2020. METHODS: Data were obtained on learners who received continuing education credits upon completion of the NIOSH Training for Nurses. We applied a widely used implementation and evaluation framework, RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance), to describe the potential reach of the nurses' training and provide descriptive statistics of learners. RESULTS: From 2015 to 2020, 7899 learners from different occupations received continuing education credits for completing the training. Approximately 60% of learners were nurses and 30% were students. Among nurses, most were Registered Nurses (93%), with few Licensed Practical Nurses (6%) and Advanced Practice Nurses (2%). In 2020, the number of learners who were nurses represented only 0.09% of all licensed US nurses. CONCLUSION: A renewed dissemination plan may help extend training reach to the larger population of licensed US nurses. The NIOSH training remains a seminal, freely available, online resource for nurses, filling a critical gap in training to manage work-related fatigue.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".