Lavender aromatherapy for sleep in hospitalized patients: A scoping review
Bibliographic record
Abstract
Background and objective: The importance of promoting sleep for hospitalized patients is vital especially because sleep has been shown to promote healing. Complementary and alternative medicine approaches for health and wellness have been widely used in the United States. However, for hospitalized patients, while aromatherapy such as lavender is often used by nurses for comfort and sleep, the evidence for specific dosage and administration methods are unclear, even though its effectiveness has been shown and published. The purpose of this scoping review is to highlight the significant issues surrounding the evidence to date for lavender aromatherapy’s clinical use for hospitalized patients.Methods: This review utilized the PRISMA steps to identify literature important to the study’s purpose.Results: After the initial search using specific keywords yielded 588 articles, further steps in the process resulted in 8 studies whose purpose was to test the effectiveness of lavender aromatherapy for sleep in hospitalized patients. Three major categories that resulted from the review addressed the clinical evidence limitations associated with lavender’s use for sleep: the wide range of sample characteristics and counties, mixed variables for study purposes, and disparate dosage and administration methods. Most significant for clinical practice was the disparate dosages and methods of lavender aromatherapy administration across the studies reviewed for effectiveness.Conclusion and implications: Nurses should proceed with caution when using lavender aromatherapy for hospitalized patients. This review highlighted the need for nurses to conduct and disseminate findings from randomized clinical trials utilizing hospitalized general medical-surgical patients. Testing dosages and administration methods that have shown to be effective for medical surgical hospitalized patients is warranted.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".