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Enregistrement W4402853955 · doi:10.1093/eurjcn/zvae123

The nurse, the framework, and the digital future

2024· article· en· W4402853955 sur OpenAlexaff
Nicola Straiton, Sandra Lauck, Krystina B. Lewis

Notice bibliographique

RevueEuropean Journal of Cardiovascular Nursing · 2024
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealth Policy Implementation Science
Établissements canadiensUniversity of OttawaSt. Paul's HospitalUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineNursing

Résumé

récupéré en direct d'OpenAlex

This invited commentary refers to the ‘Digital and technological solutions in cardiovascular nursing and perspectives for a smooth digital shift: a discussion paper’, by G. Conte et al., https://doi.org/10.1093/eurjcn/zvae096. Digital and technological solutions (DTS) such as telehealth, mobile apps, TeleECG, wearables, and electronic health records are gradually transforming cardiovascular nursing care by integrating data-driven approaches, improving patient outcomes, and fostering collaboration. While advocating for the integration of DTS into cardiovascular care, the recent discussion paper by Conte et al.1 aptly underscores the need to address ongoing challenges such as data standardization, digital literacy, privacy, clinician training, and ethical considerations to enable effective implementation. Using evidence-based theoretical frameworks, as proposed by Conte et al., to address factors such as individual characteristics, external conditions, knowledge, and attitudes offers a practical method for overcoming challenges and successfully implementing innovations in practice. Even with a strong evidence base for a healthcare intervention—whether it is a model of care, a new treatment, or a DTS—clinician and practice changes will not occur effectively or efficiently without intentional and deliberate implementation efforts.2 This may include, for example, acquiring robust implementation data (e.g. clinical audits and patient journey process mapping), identifying barriers and facilitators to the intervention’s uptake, selecting evidence-based implementation strategies, and evaluating outcomes. Implementation science involves the scientific study of methods and strategies to promote the uptake of evidence-based practice (EBP) and research into routine use by practitioners and policymakers.3 This field emerged to tackle the challenges of translating research into practical applications in healthcare and other sectors. A key factor driving the growth of this area of science was the recognition that early implementation research, while often designed and conducted with best intentions, often relied on ‘an expensive version of trial-and-error’ approach and tended to prioritize empirical outcomes over the foundational importance of theoretical frameworks.4 As Nilsen5 highlights, this lack of theoretical grounding makes it challenging to understand and explain why implementation succeeds or fails, thereby limiting our ability to identify predictors of success, develop more effective strategies for future implementation of healthcare interventions, and sustain its use. Theoretical approaches in implementation science serve three primary purposes: specifying and guiding the process of translating research into practice (process models); understanding and explaining the factors that influence implementation and outcomes (determinant frameworks, classic theories, implementation theories); and assessing the effectiveness of implementation efforts (evaluation frameworks).6 Theoretical frameworks offer the structural support needed to organize and apply these theories in research. For example, determinant frameworks can guide data collection and analysis to identify factors (determinants such as barriers/enablers) influencing implementation, which can then inform the development of implementation strategies.7 In a study by Crozier et al.,8 this approach was applied using the Consolidated Framework for Implementation Research (CFIR) to investigate the role of clinical exercise physiologists in UK cardiac rehabilitation services. Researchers used the framework to formulate research questions and design data collection and analysis exploring the integration of these professionals into cardiac rehabilitation services, focusing on staffing structures, skills, competencies, and patient perceptions. This approach provided valuable insights for the future implementation and evaluation of these roles within the clinical service. Conte and colleagues are to be commended for recently developing the Digitech-F conceptual framework, a robust approach aimed at improving the adoption of DTS in nursing by addressing key aspects such as skills, knowledge, attitude, and competence among healthcare professionals. Additionally, frameworks such as the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) have also gained prominence for guiding the implementation of healthcare technologies.9 The i-PARIHS framework identifies four essential components—facilitation, innovation, recipients, and context—that are vital for the successful implementation of healthcare interventions.10 It has been effectively used to support the integration of digital health technologies across various settings. For instance, a review of mental health smartphone apps, by Connolly et al.11 used the i-PARIHS framework to organize enablers and barriers to their implementation at the level of the innovation (the smartphone apps), its intended recipients, and the context in which they were to be implemented. A key finding from this review highlighted that smartphone ownership alone is not a reliable predictor of app use; instead, factors such as a patient’s data and Wi-Fi capabilities must be considered by providers and the need for more education and resources by both clinicians and patients around how to identify effective and evidence-based mental health applications. This example illustrates the practical application of the i-PARIHS framework in guiding both clinical practice and research in healthcare intervention implementation. As Conte aptly underscores, nurses, as the largest professional group in healthcare, possess substantial potential to translate evidence into practice, particularly in the context of digital and technological solutions. Hence, integrating a robust theoretical foundation into their clinical and research curricula is both strategic and imperative for those interested in integrating implementation science into cardiovascular research; nurses bring valuable insights into the practical challenges and facilitators of designing effective, pragmatic studies to investigate theory-informed approaches for implementing evidence-based interventions. Similarly, nurses with a strong clinical focus—who are deeply familiar with the clinical context and patient journeys due to their close, ongoing interaction with patients—could train and practice as implementation practitioners.12 By leveraging their clinical expertise and deep understanding of the healthcare setting, they could effectively apply research findings in real-world scenarios, driving the adoption of best practices and innovative solutions within dynamic clinical environments. In summary, integrating evidence-based implementation strategies into cardiovascular care, grounded in clear theoretical frameworks, would allow healthcare professionals, system leaders, patients, and industry partners to accelerate the adoption of DTS. This approach would enable the consistent application of evidence-based practices across diverse clinical settings and patient populations, facilitating timely and equitable access to innovations for those who will benefit most. Nicola Straiton, PhD, MSc (RN BSc (Hons)) (Conceptualization [lead]; Writing—original draft [lead]), Sandra B Lauck, PhD, RN (Writing—review & editing [equal]), and Krystina B Lewis, PhD, RN (Writing—review & editing [equal]). No new data were generated or analysed in support of this research.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,020
Score d'incertitude au seuil0,106

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0200,023
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0070,038
Communication savante0,0200,023
Science ouverte0,0010,009
Intégrité de la recherche0,0080,006
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,134
Tête enseignante GPT0,516
Écart entre enseignants0,382 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentnon

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