Patients as partners in health research: A scoping review
Bibliographic record
Abstract
BACKGROUND: The role of patient involvement in health research has evolved over the past decade. Despite efforts to engage patients as partners, the role is not well understood. We undertook this review to understand the engagement practices of patients who assume roles as partners in health research. METHODS: Using a recognized methodological approach, two academic databases (MEDLINE and EMBASE) and grey literature sources were searched. Findings were organized into one of the three higher levels of engagement, described by the Patient and Researcher Engagement framework developed by Manafo. We examined and quantified the supportive strategies used during involvement, used thematic analysis as described by Braun and Clarke and themed the purpose of engagement, and categorized the reported outcomes according to the CIHR Engagement Framework. RESULTS: Out of 6621 records, 119 sources were included in the review. Thematic analysis of the purpose of engagement revealed five themes: documenting and advancing PPI, relevance of research, co-building, capacity building and impact on research. Improved research design was the most common reported outcome and the most common role for patient partners was as members of the research team, and the most commonly used strategy to support involvement was by meetings. CONCLUSION: The evidence collected during this review advanced our understanding of the engagement of patients as research partners. As patient involvement becomes more mainstream, this knowledge will aid researchers and policy-makers in the development of approaches and tools to support engagement. PATIENT/USER INVOLVEMENT: Patients led and conducted the grey literature search, including the synthesis and interpretation of the findings.
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 | 0.067 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".