Bibliographic record
Abstract
It is now well recognized that, despite considerable investment in the generation of research, for the most part it is not routinely used in practice or policy. The inimitable context of healthcare practice is certainly a contributing factor as it continues to increase in uncertainty, instability and complexity.1 However, with a small investment in building partnerships, there is the potential to achieve significant impacts at the point of care. To achieve delivery of healthcare that is based on the best available evidence, we cannot work in isolation and academic–clinical partnerships offer opportunities to leverage expertise in both sectors to reach this goal. The Joanna Briggs Institute (JBI) Model of Evidence Based Healthcare recognizes that theory and practice are equally important parts of the same agenda and for evidence-based decision-making to occur in an authentic manner it is critical that the evidence is clinically relevant.2 On the contrary, academic and clinical settings do not always align as they could or should and shifting priorities, funding sources and organizational structures can make the creation of meaningful partnerships challenging. Implementation of a formalized framework around clinical partnerships to overcome fundamental differences in academic and health service culture and orientation around evidence-based healthcare could be beneficial in overcoming these challenges. In a similar vein to the Canadian National Institutes of Health practice-based research networks, these partnerships would offer a two-way connection ‘between the interstates of academic scientific discoveries and the patients receiving care in the ambulatory practice’.3 Gaglioti et al.4 believe that these partnerships offer an opportunity to ‘synergize around shared values and goals, and eventually serve as a bridge connecting those working on issues from the towers of policy or academia to those with “boots on the ground” experience’. Similarly, in the United Kingdom, the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) program, funded through the National Institute for Health Research, seeks to address the problem of translation from research-based evidence to routine healthcare practice through partnership.5 Indeed, the CLAHRC program has also initiated an Evidence Synthesis Collaboration to facilitate the use of evidence syntheses by CLAHRC partners by ensuring the syntheses projects are driven by user needs. An Evidence for Change pilot was undertaken in 2015 that sought to encourage the use of the best available evidence to inform professional practice change. The JBI takes the view that a synergistic approach to this endeavor will result in a valuable and powerful approach to addressing translational gaps and achieving an evidence-based approach to the healthcare delivery. For this to occur, the knowledge needs of expert clinicians need to be paired with the skills of experienced academics to synthesize and produce practical, relevant, usable evidence. If evidence-based healthcare is approached as an organizational, collaborative initiative, from synthesis to transfer and implementation, then the potential for increased sustainability and improved health outcomes is surely increased. The international Joanna Briggs Collaboration consists of JBI Centres of Excellence and Affiliated Groups that contribute to furthering the vision and mission of the institute globally through the delivery of high-quality programs of evidence synthesis, transfer and implementation. The academics in these entities agree that health professionals on the frontline of service provision have unique insights to offer the evidence-based endeavor and that academic–clinical partnerships will form a strong foundation for the planning and delivery of evidence-based services. In partnering with a JBI Collaborating Entity, there is significant potential for capacity building of stakeholders and to enhance the learning culture of both settings. Kitson et al.6 remind us of the complexity of knowledge translation and encourage us to think about the role of actors (stakeholders), relationships and networks to actively mobilize knowledge between those involved and to embrace collaborative processes of knowledge production and use. Knowledge translation is not a perfect science, far from it. However, it can surely only be strengthened by finding the synergies across policy, practice and research. Acknowledgements Conflicts of interest The author reports no conflicts of interest.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".