Self-supporting wound care mobile applications for nurses: A scoping review protocol
Bibliographic record
Abstract
AIM: Mobile health (mHealth) is playing an increasingly important role in the computerization of wound care on an international scale with an aim to improve care. The aim of this scoping review protocol is to present a transparent process for how we plan to search and review the existing evidence related to self-supporting mobile wound care applications used by nurses. MATERIALS AND METHODS: The scoping review will follow the Joanna Briggs Institute (JBI) methodology. An exploratory search was performed using MEDLINE (Ovid), Embase, CINAHL (Ebsco), to identify concepts, keywords, MeSH terms, and headings to identify study types looking for mobile applications in wound care. The findings of this search will determine the final search strategy. Data sources will include MEDLINE, Embase, CINAHL, Web of Science, LiSSa, Cochrane Wounds (Cochrane Library) and Erudit. The titles and abstracts of the identified articles will be screened independently by two authors for relevance. Full texts will also be screened by two independent reviewers and data extraction will be performed in accordance with a pre-designed extraction form. All types of studies and literature linked to self-supporting mobile wound care application used by nurses will be included (quantitative, qualitative, mixed methods and grey literature). CONCLUSION: The results of the scoping review will give an overview of the existing self-supporting mobile applications in wound care used by nurses. These will also help to identify the existing applications, and describe knowledge in nursing about their utilisation, development, and evaluation, as well as synthesize the available literature on their impacts.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.133 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.026 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".