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Record W4390664495 · doi:10.1111/jan.16052

Self‐supporting wound care mobile applications for nurses: A scoping review

2024· review· en· W4390664495 on OpenAlexafffund
Julie Gagnon, Sebastian Probst, Julie Chartrand, Emily Reynolds, Michelle Lalonde

Bibliographic record

VenueJournal of Advanced Nursing · 2024
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsInstitut du Savoir MontfortChildren's Hospital of Eastern OntarioUniversité du Québec à RimouskiMontfort HospitalUniversity of Ottawa
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsCINAHLDocumentationWound careMEDLINEMedicineHealth careCompetence (human resources)NursingGrey literatureCochrane LibraryMobile technologyMobile deviceMedical educationPsychologyComputer scienceWorld Wide WebAlternative medicinePsychological intervention

Abstract

fetched live from OpenAlex

AIM: This study provides an overview of the literature to identify and map the types of available evidence on self-supporting mobile applications used by nurses in wound care regarding their development, evaluation and outcomes for patients, nurses and the healthcare system. DESIGN: Scoping review. REVIEW METHOD: Joanna Briggs Institute scoping review methodology was used. DATA SOURCES: A search was performed using MEDLINE, Embase, CINAHL (via EBSCO), Web of Science, LiSSa (Littérature Scientifique en Santé), Cochrane Wounds, Érudit and grey literature, between April and October 2022, updated in April 2023, to identify literature published in English and French. RESULTS: Eleven studies from 14 publications met the inclusion criteria. Mostly descriptive, the included studies presented mobile applications that nurses used, among other things, to assess wounds and support clinical decision-making. The results described how nurses were iteratively involved in the process of developing and evaluating mobile applications using various methods such as pilot tests. The three outcomes most frequently reported by nurses were as follows: facilitating care, documentation on file and access to evidence-based data. CONCLUSION: The potential of mobile applications in wound care is within reach. Nurses are an indispensable player in the successful development of these tools. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: If properly developed and evaluated, mobile applications for wound care could enhance nursing practices and improve patient care. The development of ethical digital competence must be ensured during initial training and continued throughout the professional journey. IMPACT: We identified a dearth of studies investigating applications that work without Internet access. More research is needed on the development of mobile applications in wound care and their possible impact on nursing practice in rural areas and the next generation of nurses. REPORTING METHOD: The Preferred Reporting Items for Systematic Reviews and Meta-analysis Extension for Scoping Review guidelines were used. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0250.019
Science and technology studies0.0020.001
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.078
GPT teacher head0.586
Teacher spread0.507 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2024
Admission routes2
Has abstractyes

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