Engaging family partners in deceased organ donation research—a reflection on one team’s experience
Bibliographic record
Abstract
PURPOSE: Clinical researchers are now encouraged to include patient partners in all research projects. Nevertheless, published accounts of patient engagement in complex research projects, such as those involving critically ill and dying patients, are lacking. Whether this absence is due to the relatively new emergence of patient engagement research methods or fundamental challenges regarding family engagement in challenging research contexts is unclear. We describe our experiences with forming a researcher-family partnership in a deceased organ donation research project involving the prospective observation of potential and actual deceased organ donors dying in the intensive care unit. METHODS: We used the Guidance for Reporting Involvement of Patients and the Public evidence-based, consensus-informed reporting guidelines to organize our narrative. RESULTS: We were able to initiate and sustain a research consultant relationship with the mother of a deceased organ donor for over two years. Challenges faced included: constraints on money and time, communication preferences, and the emotional stress of participating in difficult conversations. Positive outcomes included: improvement of data collection tools, new opportunities for access to research populations, and motivation to include family partnership in future grant proposals. CONCLUSIONS: Family engagement in deceased organ donation research is feasible and contributes positively to study progress and outcomes. Patient and family engagement in challenging research contexts may require special attention to the emotional challenges of participation. We hope that our experience will encourage clinical researchers working in deceased organ donation and similarly complex domains to consider including patient partners in their projects.
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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 | 0.055 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.016 |
| 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".