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Record W4403685157 · doi:10.2196/58030

Advancing Digital Education Technologies by Empowering Nurses With Point-of-Care Ultrasound: Protocol for a Mixed Methods Study

2024· article· en· W4403685157 on OpenAlexvenueno aff
Fernanda Raphael Escobar Gimenes, Angelita Maria Stabile, Rodrigo Magri Bernardes, Vinícius Batista Santos, Mayra Gonçalves Menegüeti, Patrícia Rezende do Prado, Maurício Serra Ribeiro, Flávia Giron Camerini, Soraia Assad Nasbine Rabeh

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistContent validityMedicineMedical educationProtocol (science)Construct validityMultidisciplinary approachSystematic reviewHealth careNursingMEDLINEPsychologyAlternative medicinePatient satisfactionPathologyPsychometrics

Abstract

fetched live from OpenAlex

BACKGROUND: Bedside ultrasonography, also known as point-of-care ultrasound (PoCUS), is a promising technological tool that enhances clinical assessment, enriching diagnostic capabilities and clinical reasoning. Its use in nursing spans various patient populations and health care settings, providing nurses with a valuable health assessment tool to improve care quality and patient safety. Despite its growing integration into clinical practice, PoCUS training has mainly focused on physicians, leaving a gap for trained nurses who demonstrate similar proficiency in conducting scans and interpreting images. Previous research highlights the value of digital tools in PoCUS training, showing their role in improving professionals' and students' knowledge, image interpretation skills, and clinical acumen. OBJECTIVE: This study aimed to (1) establish an assessment instrument gauging nurses' competency milestones in PoCUS and evaluate its content and appearance validity, (2) develop a series of 5 educational videos focused on PoCUS and assess their content and appearance validity, and (3) construct an online learning environment tailored to nurses' PoCUS training needs and evaluate its content and appearance validity. METHODS: We will conduct a methodological study of technological production guided by Rogers' diffusion of innovations theory. Subproject 1 will design and validate a comprehensive assessment tool for evaluating nurses' competency milestones in PoCUS use. For this purpose, a scoping review will be conducted. The review will be based on JBI Collaboration guidelines and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extended for Scoping Reviews (PRISMA-ScR) checklist. Subproject 2 involves an evaluation of content and appearance validity for a series of 5 educational videos on PoCUS, designed specifically for nurses about applying peripherally inserted central catheter lines, inserting nasogastric feeding tubes, assessing gastric residual volume, assessing pressure injuries and soft tissue conditions, and assessing muscle mass to monitor patient nutritional status. In subproject 3, a comprehensive online learning environment dedicated to PoCUS training for nurses will be developed and validated. The launch of an online learning environment represents a cornerstone of our dissemination strategy, scheduled to coincide with the inaugural Brazilian PoCUS symposium for nurses, an event organized by the project members. This platform will serve as a pivotal resource for continuous learning and professional development. RESULTS: Subproject 1 will start in the second half of 2024 and is expected to be completed by mid-2025. Subproject 2 is currently ongoing and is expected to be completed in early 2026. Subproject 3 is set to begin in early 2025 and is planned to be completed by 2026. CONCLUSIONS: Through these concerted efforts, the project aims to bridge the existing gap in PoCUS training for nurses, thereby fostering their proficiency and enhancing patient care outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/58030.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.828
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.636
Teacher spread0.501 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2024
Admission routes1
Has abstractyes

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