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Record W4396957559 · doi:10.1186/s12912-024-02003-x

Content of a wound care mobile application for newly graduated nurses: an e-Delphi study

2024· article· en· W4396957559 on OpenAlexafffund
Julie Gagnon, Julie Chartrand, Sebastian Probst, Michelle Lalonde

Bibliographic record

VenueBMC Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
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
KeywordsMedicineNursing managementNursing researchDelphi methodNursingHealth informaticsPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: Wound care represents a considerable challenge, especially for newly graduated nurses. The development of a mobile application is envisioned to improve knowledge transfer and facilitate evidence-based practice. The aim of this study was to establish expert consensus on the initial content of the algorithm for a wound care mobile application for newly graduated nurses. METHODS: Experts participated in online surveys conducted in three rounds. Twenty-nine expert wound care nurses participated in the first round, and 25 participated in the two subsequent rounds. The first round, which was qualitative, included a mandatory open-ended question solicitating suggestions for items to be included in the mobile application. The responses underwent content analysis. The subsequent two rounds were quantitative, with experts being asked to rate their level of agreement on a 5-point Likert scale. These rounds were carried out iteratively, allowing experts to review their responses and see anonymized results from the previous round. We calculated the weighted kappa to determine the individual stability of responses within-subjects between the quantitative rounds. A consensus threshold of 80% was predetermined. RESULTS: In total, 80 items were divided into 6 categories based on the results of the first round. Of these, 75 (93.75%) achieved consensus during the two subsequent rounds. Notably, 5 items (6.25%) did not reach consensus. The items with the highest consensus related to the signs and symptoms of infection, pressure ulcers, and the essential elements for healing. Conversely, items such as toe pressure measurement, wounds around drains, and frostbite failed to achieve consensus. CONCLUSIONS: The results of this study will inform the development of the initial content of the algorithm for a wound care mobile application. Expert participation and their insights on infection-related matters have the potential to support evidence-based wound care practice. Ongoing debates surround items without consensus. Finally, this study establishes expert wound care nurses' perspectives on the competencies anticipated from newly graduated nurses.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.158
GPT teacher head0.504
Teacher spread0.346 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes2
Has abstractyes

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