Operationalizing a patient engagement plan for health research: Sharing a codesigned planning template from a national clinical trial
Bibliographic record
Abstract
INTRODUCTION: Engaging with patients about their lived experience of health and illness and their experience within the healthcare system can help inform the provision of care, health policies and health research. In the context of health research, however, operationalizing the levels of patient engagement is not straightforward. We suggest that a key challenge to the routine inclusion of patients as partners in health research is a lack of tangible guidance regarding how this can be accomplished. METHODS: In this article, we provide guidance on how to codesign and operationalize a concrete patient engagement plan for any health research project. RESULTS: We illustrate a seven-step approach using the example of a national clinical trial in Canada and provide a patient engagement planning template for use in any health research project. CONCLUSION: Such concrete guidance should improve the design and reporting of patient engagement in health research. PATIENT OR PUBLIC CONTRIBUTION: The De-Implementing Wisely Research group is informed by a national 9-member patient partner council (PPC). The research team includes three lead patient partners who are coinvestigators on the grant that funds the program of research. Members of the council advise on all aspects of the study design and implementation. The ideas presented in this paper were informed by regular communication and planning with the PPC; specific contributions of lead patient partner authors are outlined as follows: Brian Johnston, Susan Goold and Vanessa Francis are patient partners with a wide breadth of experience in the healthcare system and health research projects. The guidance in this article draws on their lived and professional expertise. All patient partner authors contributed to the planning of the manuscript, participated in meetings to develop content and provided critical manuscript edits and comments on drafts.
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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 | 0.442 | 0.466 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".