Researchers’ experiences with patient engagement in health research: a scoping review and thematic synthesis
Bibliographic record
Abstract
CONTEXT: Implicating patients in research is gaining popularity around the world and is now the reference of many funding agencies. Understanding these partnerships is necessary to grasp this new reality. The experiences of researchers who have involved patient-partners (PPs) in health research are important for a better understanding of these practices. OBJECTIVE: This study aimed to identify and analyze the existing qualitative scientific literature on the experiences of academic researchers involved in health research with patient engagement (PE). DESIGN: A scoping review of the available literature with an inductive thematic synthesis, guided by the methodological framework of Arksey and O'Malley. DATA COLLECTION: A search strategy was developed to include keywords relating to researchers, patient-partners, experiences, and the qualitative methodologies of the targeted studies. Five databases were searched using the EBSCO-host engine. The search results were screened by four reviewers to only include articles written in English on the topic of the experience of academic researchers having worked with PPs in health research based on qualitative studies or mixed-methods studies with a distinct qualitative section. ANALYSIS: Articles included were charted for general information. All "results" sections were coded line by line. These codes were organized inductively to form descriptive and analytical themes. This led to the synthesis of the ideas found in the selected articles. RESULTS: The search strategy yielded 7616 results, of which 2468 duplicates were removed. The remaining 5148 articles were screened, resulting in the exclusion of 5114 off-topic studies. The remaining 29 full-text articles were evaluated for inclusion from which 5 additional studies were identified. The final selection consisted of 11 articles that met all the criteria. These articles were published between 2009 and 2019. Five general themes inductively emerged from the analysis: the understanding of PE, motivations, contexts, attitudes, and practical aspects of PE that are central to researchers. CONCLUSION: This scoping review provides a better understanding of the experiences of researchers who have implemented patient partnerships in health research projects. Our findings reveal many positive elements central to health researchers' discourses about PE, but they provide insights into the challenges and postures of resistance. This knowledge can support the development of empirically sound improvements in PE practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".