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Leveraging nursing research to transform healthcare systems

2008· editorial· en· W2091124528 on OpenAlexaff
Nancy Edwards

Bibliographic record

VenueNursing Inquiry · 2008
Typeeditorial
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsNursing shortageNursingNurse educationNursing researchHealth careDeclarationMedicinePolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

Calls to strengthen the generation and use of nursing science are not new. Prominent nursing leaders (Antrobus and Kitson 1999; Alcock and Arthur 2002; PAHO 2004), the Munich Declaration (WHO/Europe 2000), and the World Health Assembly (2006) have emphasized the need for nursing research to inform healthcare decision-making. Despite repeated calls for action, progress has been slow. And now, we find ourselves at a critical junction; the encroaching worldwide nursing shortage has the potential to undermine the gains already made. This threat is particularly worrisome in lower-income countries where existing nursing research capacity is extremely fragile and the nursing shortage is most acute. In sub-Saharan Africa, for example, it is estimated that another 600 000 nurses are needed to meet the Millennium Development Goals (Buchan and Calman 2004). In the Caribbean, the outflow of migrating nurses to the UK and to North American destinations is likely to worsen as international recruitment efforts build. The purpose of this editorial is to consider the current state of nursing research and discuss how we might maintain momentum through the pending challenges of nursing shortages. Many have worked to establish research as a critical nursing function. Increasingly, in both baccalaureate and diploma programmes around the world, nursing students are introduced to the basics of research as a foundation of their education. Research capacity has grown with the establishment of graduate programmes for nurses, and efforts to increase the financial and geographical accessibility of these programmes. Although these initiatives remain concentrated in higher-income countries, colleagues in many lower- and middle-income countries are leading important collaboratives to increase the accessibility of graduate education opportunities and to fast-track research preparation for nurses (Galvin and Courtney 2005). The essential infrastructure required to support nursing research is also growing, albeit slowly. Worldwide, obtaining research funding remains a highly competitive process that is positioned within a decision-making structure that is predominantly biomedical in its orientation (Rafferty and Traynor 2002). However, nurse scientists are demonstrating success, querying eligibility restrictions on health research funds that preclude the submission of nursing research protocols, and assembling strong teams including experienced researchers who are successfully putting forward novel and competitive projects. Gradually, nursing research is gaining entrée to decision-makers. Cumulative bodies of evidence are demonstrating the cost-effectiveness of nursing services and beginning to fuel important debates regarding healthcare investments. Although the topography of nursing research capacity, access and use is very uneven, overall, nursing research now has a foothold in the realms of decision-makers. But as we face the nursing shortage, how do we mitigate the risk that nursing research may be seen as a luxury, rather than as an essential element to inform the provision of effective and accessible healthcare services? I suggest that several strategies are needed. First, nursing research must be clearly positioned within national and international science and technology strategies. We need clear statements of the essential role of nursing science in addressing our most pressing health issues including the growing disparities between rich and poor. Second, we must create channels to communicate to decision-makers, promising and highly pertinent examples of nursing innovations that are the product of research. Researchers must pay particular attention to the potential scaling-up of new service delivery models with demonstrated effectiveness. Third, we must be prepared to support and nurture programmatic research. We need larger-scale programmes of research that build in mentorship and knowledge translation, and bridge the work of health services, clinical and population health research. The results of programmatic research are essential to inform investment decisions. We must move beyond nursing research that predominantly involves small-scale studies. Although these are often useful for local quality improvement strategies, they are rarely externally generalizable or eligible for inclusion within systematic or integrative reviews of evidence. So how do we get there? There need to be concerted efforts by leaders in nursing service, policy and research communities to provide illustrative examples of how nursing research can make a difference. We must be prepared to discuss how the products of nursing research can bring a return on investment and address the emerging challenges of nursing shortages. We need to assemble context-relevant sets of case studies illustrating the indirect and direct impact of nursing research in various parts of the world. We need prototypes of programmes in nursing research to bring to the decision-making table. It is imperative that we articulate nursing research questions that tackle clinical, service delivery and policy issues. We must remember that the world of nursing research is opaque to most. It is a world where most biomedical scientists have never ventured or been invited to explore. It is up to nurse scientists and nursing leaders to change this reality. We must engage top-level research scientists, from diverse fields, as co-mentors on our teams. This would bring the advantages of strong mentorship alongside learning opportunities for senior scientists to explore the arena of nursing research. And we must apply the most innovative and effective knowledge translation strategies to undertake the complex business of transforming nursing research evidence into practice and policy. Our future successes will require the confluence of national and global strategic visions that include building nurses’ capacity to do and use research, providing essential infrastructure to support nursing research, and orchestrating knowledge translation strategies that use nursing science as a critical input for health systems transformation. We have come too far to lose our research foothold now.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
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 head0.139
GPT teacher head0.461
Teacher spread0.322 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations6
Published2008
Admission routes1
Has abstractyes

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