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Record W4402853955 · doi:10.1093/eurjcn/zvae123

The nurse, the framework, and the digital future

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

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineNursing

Abstract

fetched live from 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.

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)0.020
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.038
Scholarly communication0.0200.023
Open science0.0010.009
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.134
GPT teacher head0.516
Teacher spread0.382 · 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 designTheoretical or conceptual
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".

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Citations0
Published2024
Admission routes1
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