Wearable Devices and Nurses’ Health: Protocol for an Integrative Review
Bibliographic record
Abstract
BACKGROUND: Nurses comprise over half of the global health care workforce, and the nursing care they provide is critical for the global population's health. High patient volumes and increased medical complexity have increased the workload and stress of nurses. As a result, the health of nurses is often negatively impacted. Wearables are used within the health care setting to assess patient outcomes; however, efforts to synthesize the use of wearable devices focusing on nurses' health are limited. OBJECTIVE: The primary objective of our integrative review is to synthesize available data concerning the utility of wearable devices for evaluating or improving (or both) the health of nurses. METHODS: We are conducting an integrative review synthesizing data specific to wearable devices and nurses' health. The research question for this review aims to answer how wearable devices are used to evaluate health outcomes among nurses. We searched the following electronic databases from inception until July 2022: PubMed, Embase, CINAHL, Web of Science, IEEE Explore, and AS&T. Titles and abstracts were imported into Covidence software, where citations were screened and duplicates removed. Title and abstract screening has been completed; however, full-text screening has not been started. Further screening is being conducted independently and in duplicate by 2 teams of 2 reviewers each. These reviewers will extract data independently. RESULTS: Search strategies have been developed, and data were extracted from 6 databases. After the removal of duplicates, we collected 8603 studies for title and abstract screening. Two independent reviewers conducted the title and abstract review, and after resolving conflicts, 277 full-text articles are available for review to determine whether they meet the inclusion criteria. CONCLUSIONS: This integrative review will provide synthesized data to inform nurses and other stakeholders about the extent of wearable device-related work done with nurses and provide direction for future research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48178.
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 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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".