Moving patient-oriented research forward: thoughts from the next generation of knowledge translation researchers
Bibliographic record
Abstract
As knowledge translation trainee participants, we report on the discussions that took place during the 2017 Knowledge Translation Canada Summer Institute. The theme of the institute was patient-oriented research and patient engagement in research. Trying to move knowledge into health care practice can be difficult. Including patients and families as members of the research team can help to overcome some of these challenges by producing more relevant research designs and results. However, in the absence of guidelines and best practices, it can be difficult for trainees and researchers to effectively engage patients and families in designing and conducting research. We detail how trainees and early career researchers are currently engaging patients in their research, the strengths and challenges of engaging patients in research, and lessons learned. These discussions have helped us to identify important areas where future training and guidance is needed to support trainees as patient-oriented researchers. Background Moving knowledge into health care practice can present a number of challenges for researchers. Including patients and families as members of the research team can help to overcome some of these challenges by producing more relevant research designs and results. However, many trainees and researchers experience difficulty in engaging patients and families in research effectively. Main body We report on the discussions that took place at the 2017 Knowledge Translation (KT) Canada Summer Institute (KTCSI). The theme of the KTCSI was patient-oriented research and patient engagement in research. We provide an important viewpoint on how trainees and early career researchers are currently engaging patients in their research, the strengths and challenges of engaging patients in research, and lessons learned. As the target audience of the KTCSI, we provide our thoughts on what is needed to support trainees and researchers to more effectively engage patients and families in research. Conclusion While many of the participants at the KTCSI are conducting patient-oriented research, practical guidance, resources and tools are needed to ensure the effective engagement of patients in research. These discussions have helped us to identify how to move forward as patient-oriented researchers and where future work and support is needed to achieve effective engagement.
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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 | 0.181 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.035 | 0.048 |
| Scholarly communication | 0.036 | 0.038 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.026 | 0.068 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".