Patient and Public Engagement in Integrated Knowledge Translation Research: Are we there yet?
Bibliographic record
Abstract
PLAIN ENGLISH SUMMARY: There have been many attempts to improve how healthcare services are developed and delivered. Despite this, we know that there are many gaps and differences in practice and that these can lead to poor patient outcomes. In addition, there are also concerns that research is being undertaken that does not reflects the realities or needs of those using healthcare services, and that the use of research findings in practice is slow. As such, shared approaches to research, such as integrated knowledge translation, are being used.Integrated knowledge translation (IKT) is a research approach that brings together researchers, along with other stakeholders that have knowledge about a particular healthcare issue. Stakeholders may include healthcare providers and policy-makers. More recently, there has been a growing awareness of the need to include patients and members of the public within research processes. These collaborative and patient-oriented research approaches are seen as a way to develop research that tackles ongoing gaps in practice and reflect the insights, needs and priorities of those most affected by health research outcomes. Despite great support, little is known about how these major research approaches are connected, or how they may bring about improvements in the development and use of research evidence. In this paper, we examine how IKT and patient engagement processes are linked, as well as exploring where differences exist. Through this, we highlight opportunities for greater patient engagement in IKT research and to identify areas that need to be understood further. ABSTRACT: Healthcare organizations across the world are being increasingly challenged to develop and implement services that are evidence-based and bring about improvement in patient and health service outcomes. Despite an increasing emphasis upon evidence-based practice, large variations in practice remain and gaps pervade in the creation and application of knowledge that improves outcomes. More collaborative models of health research have emerged over recent years, including integrated knowledge translation (IKT), whereby partnerships with key knowledge users are developed to enhance the responsiveness and application of the findings. Likewise, the meaningful engagement of patients, in addition to the inclusion of patient-reported outcomes and priorities, has been hailed as another mechanism to improve the relevance, impact and efficiency of research.Collectively, both IKT and patient engagement processes provide a vehicle to support research that can address health disparities and improve the delivery of effective and responsive healthcare services. However, the evidence to support their impact is limited and while these approaches are inextricably connected through their engagement focus, it is unclear how IKT and patient engagement processes are linked conceptually, theoretically, and practically. In this paper, we will begin to critically examine some of the linkages and tensions that exist between IKT and patient-engagement for research and will examine potential opportunities for IKT researchers as they navigate and enact meaningful partnerships with patients and the public.
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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 | 0.113 | 0.360 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.024 | 0.025 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".