Future directions for patient engagement in research: a participatory workshop with Canadian patient partners and academic researchers
Bibliographic record
Abstract
BACKGROUND: Patient engagement in research (also commonly referred to as patient or patient and public involvement in research) strives to transform health research wherein patients (including caregivers and the public) are regularly and actively engaged as multidisciplinary research team members (i.e. patient partners) working jointly towards improved health outcomes and an enhanced healthcare system. To support its mindful evolution into a staple of health research, this participatory study aimed to identify future directions for Canadian patient engagement in research and discusses its findings in the context of the international literature. METHODS: The study met its aim through a multi-meeting pan-Canadian virtual workshop. Participants (n = 30) included Strategy for Patient-Oriented Research-funded academic researchers and patient partners identified through a publicly available database, personal and professional networks and social media. All spoke English, could access the workshop virtually, and provided written informed consent. The workshop was composed of four, 1.5-3-h virtual meetings wherein participants discussed the current and preferred future states of Canadian patient engagement in research. Workshop discussions (i.e. data) were video and audio recorded. Themes were generated through an iterative process of inductive thematic analysis that occurred concurrently with the multi-week workshop. RESULTS: Our participatory and iterative process identified 10 targetable areas of focus for the future of Canadian patient engagement in research. Five were categorized as system-level (systemic integration; academic culture; engagement networks; funding models; compensation models), one as researcher-level (engagement processes), and four crossed both levels (awareness; diversity and recruitment; training, tools and education; evaluation and impact). System level targetable areas called for reshaping the patient engagement ecosystem to create a legitimized and supportive space for patient engagement to be a staple component of a learning health system. Researcher level targetable areas called for academic researchers and patient partners to collaboratively generate evidence and apply knowledge to inform values and behaviours necessary to foster and sustain supportive health research spaces that are accessible to all. CONCLUSIONS: Future directions for Canadian patient engagement in research span 10 interconnected targetable areas that require strong leadership and joint action between patient partners, academic researchers, and health and research institutions if patient engagement is to become a ubiquitous component of a learning health system.
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 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 | 0.127 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.051 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".