Moving evidence from publication to practice: opportunities for accelerating knowledge translation in cardiovascular care
Bibliographic record
Abstract
The academic impact of the European Journal of Cardiovascular Nursing is undisputed and continues to grow—consistently rated in the top 10 nursing scientific publications, the quality of the scholarship published in the journal is widely and highly regarded. The sustained high impact factors achieved over the years attest to the excellence of our scientific contributors who make EJCN the ‘go-to’ publishing destination for cardiovascular nurses and allied health professionals. And yet, there are far too many discouraging statistics about the time lag between the production of this body of good evidence and its implementation in patient care. The delay between knowledge and action is estimated to be around 17 years in health care.1 Furthermore, evidence produced in nursing and allied health research remains largely descriptive and often fails to impact clinical practice.2–4 It is imperative that we, as researchers in applied-based disciplines, do better. Given the journal’s mandate to guide contemporary practice in applied-based disciplines, we are driven to close this gap and accelerate the pace between the publication of research and its uptake in clinical care and health policy. This is what is broadly referred to as knowledge translation (KT). Knowledge translation describes the exchange between knowledge producers and knowledge users to understand, synthesize, share and apply evidence to accelerate the benefits of research to strengthen health systems and improve people’s health.5 The concept of KT was initially proposed and championed to accelerate the benefits of research through improved health, more effective services and products, and a strengthened health care system. As an umbrella term, it is used in varying ways and covers a wide range of activities that include knowledge dissemination, technology transfer, knowledge utilization, and implementation science. Most importantly, KT is a call to action for researchers (and research funders) to create more opportunities for impactful interactions with knowledge users, and more accountability for getting their research off the shelf and closer to the bedside, the clinic room or the community. Contributors to the EJCN share this commitment to accelerating change—this is evident in the publication of studies that address pressing needs to inform cardiovascular care, the thoughtful contributions to the ‘Highlights’ and ‘Novelty’ boxes, and the enthusiasm for the recent inclusion of graphical abstracts. The claim that there are two solitudes that separate active producers and passive users does not reflect the published pages, the clinical and research imperatives, or the direction we want to take. To this end, the EJCN is building on the success of the Methods Corner,6Patient Perspectives and the recently launched Science for Patients,7 and implementing the KT Corner to strengthen the journal’s commitment to our readers who share an interest in the overlapping spheres of research, clinical care, and health policy. The objectives of the KT Corner are two-fold: We aim to provide readers with (i) clinical tools, information or resources to accelerate the uptake of published evidence and (ii) insights into the ‘how to do’ KT across various contexts of cardiovascular care. Together, these strategies aim to accelerate and strengthen the impact of published research on clinical care and the delivery of health services, and demonstrate the narrowing gap between scholarship and practice. We are excited that this innovative addition to the EJCN will further increase the relevance and impact of the science published, and ensure we do not miss opportunities to move publications to practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.104 | 0.312 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.029 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".