How to become partners. Ways to enhance the quality of patient and public involvement in healthcare research
Bibliographic record
Abstract
There is a growing emphasis on involving patients and the public in healthcare research. This is especially true in qualitative healthcare research, where partnerships are encouraged between patients with lived experiences and researchers with academic expertise. The rationale is that collaboration can enhance the study's relevance to healthcare users and improve the research quality. However, establishing partnerships can be complex and challenging, requiring negotiation and alignment of expectations. In a qualitative study exploring communication in clinical encounters at a Danish university hospital, we invited patients and relatives to become involved in research. This commentary discusses the challenges, insights, and adjustments to our research design that emerged from the process. Through continuous dialogues with various patients and relatives, we, as researchers, gained a deeper understanding of how to make our research relevant to patients and relatives and how to approach involving patients and relatives in our research. By emphasizing the significance of these dialogues, we aim to demonstrate how aligning expectations and building partnerships with patients and relatives resulted in valuable learning experiences for the researchers and considerably impacted the study's design. Furthermore, we want to highlight that building partnerships requires time, flexibility, and a mutual learning approach to negotiate and align expectations effectively. In this commentary we first review the practice of involving patients and the public in healthcare research and provide an overview of the study's context. Next, we outline our efforts to negotiate and align expectations with patients and relatives, highlighting how new insights led to adjustments to the research design. Finally, we address challenges and the requirements researchers face when involving patients and the public in research partnerships.
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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 | 0.273 | 0.324 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.023 | 0.058 |
| Scholarly communication | 0.044 | 0.091 |
| Open science | 0.006 | 0.046 |
| Research integrity | 0.028 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".