Partnering with patients in healthcare research: a scoping review of ethical issues, challenges, and recommendations for practice
Bibliographic record
Abstract
BACKGROUND: Partnering with patients in healthcare research now benefits from a strong rationale and is encouraged by funding agencies and research institutions. However, this new approach raises ethical issues for patients, researchers, research professionals and administrators. The main objective of this review is to map the literature related to the ethical issues associated with patient partnership in healthcare research, as well as the recommendations to address them. Our global aim is to help researchers, patients, research institutions and research ethics boards reflecting on and dealing with these issues. METHODS: We conducted a scoping review of the ethical issues and recommendations associated with partnering with patients in healthcare research. After our search strategy, 31 peer reviewed articles published between 2007 and 2017 remained and were analyzed. RESULTS: We have identified 58 first-order ethical issues and challenges associated with patient partnership in research, regrouped in 18 second-order ethical themes. Most of the issues are transversal to all phases and stages of the research process and a lot of them could also apply to patient-partnership in other spheres of health, such as governance, quality improvement, and education. We suggested that ethical issues and challenges of partnered research can be related to four ethical frameworks: 1) Research ethics; 2) Research integrity; 3) Organizational ethics, and 4) Relational ethics. CONCLUSIONS: We have identified numerous ethical issues associated with the recent approach of patient-partnership in research. These issues are more diverse than the issues associated with a more traditional research approach. Indeed, the current discussion on how we address ethical issues in research is anchored in the assumption that patients, as research participants, must be protected from risk. However, doing research with, and not on, the patient involves changes in the way we reflect on the ethical issues associated with this approach to research. We propose to broaden the ethical discussion on partnered research to not only rely on a research ethics framework, but to also frame it within the areas of research integrity, organizational ethics and relational ethics.
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 | 0.186 | 0.368 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".