The experience of patient partners in research: a qualitative systematic review and thematic synthesis
Bibliographic record
Abstract
CONTEXT: Patient engagement in research consists in involving patients as partners across the research cycle. This practice has quickly become an international standard, with funding bodies actively encouraging it. As the increased incentive to engage patients can lead to tokenistic partnerships, it is important to consider the experiences of patient-partners. OBJECTIVE: To synthesize the qualitative literature on the experience of patients as partners in research. DESIGN: A systematic review of the literature with thematic synthesis was realized, guided by the framework developed by Thomas and Harden (Bmc Med Res Methodol 8: 45, 2008). DATA COLLECTION: A search strategy was developed to encompass keywords relating to patient-partners in research, their experience, and the qualitative nature of the target studies. 10 databases were searched using the EBSCO-host engine, along with the Scopus engine to include EMBASE. The search results were screened for the following inclusion criteria: articles written in English; articles reporting on the experience of patient-partners in research; qualitative studies or mixed-methods studies with a distinct qualitative section. ANALYSIS: Included articles were charted for general information. The CASP qualitative checklist was used for critical appraisal. The "results" section of each article was coded line by line. Codes were aggregated inductively to form descriptive themes and analytical themes, in order to synthesize the ideas found in the selection of articles. RESULTS: The initial search yielded 10,222 results. After the removal of duplicates, 5534 titles and abstracts were screened, 88 full-text reports were evaluated, and 41 studies were included. Articles reporting on these studies were published between 2005 and 2020. Seven themes emerged from the analysis: "motivations to engage in research", "activities in patient engagement", "structure", "competence", "team dynamics", "impacts on broader life", and "illness". Articles reported varying degrees of perceived impact on research and satisfaction concerning the level of engagement. The importance of power differentials and team dynamics were widely stated. CONCLUSIONS: Findings provide an in-depth view of the experiences of patient-partners in research. Most articles reported a generally positive experience, but challenges and pitfalls of patient engagement were identified. This will serve research teams by highlighting good practices and possible improvements.
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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 | 0.165 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.027 | 0.030 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".