Organ donation: A cross-Canada perspective of critical care nursing practice
Bibliographic record
Abstract
Aim: Our aim in this study was to describe the experiences of critical care nurses in the organ donation process in selected units across Canada. Interviews and focus groups were conducted to elicit perceptions of critical care nurses regarding their experiences with potential organ donors and their families. Methods: Two adult critical care units (one with an active transplant program and one with no transplant program) in each of eight Canadian cities were studied. Purposive sampling was used to select three critical care nurses from each unit for individual interviews and six to eight nurses from the 16 critical care units for focus groups. Findings: There were 112 participants who participated in an individual interview and/or a focus group. Following data analysis, the themes identified were related to support, the process of organ donation (including preparing family and ourselves, lived experience of nurses, saying good-bye, death rituals, spiritual beliefs, and meaning of death), systemic considerations (culture and environment), and outcomes of the organ donation process. Conclusion: While the benefits of organ donation and transplantation are clear, it appears that greater consideration can be given to policies, structures and processes, including education about systemic racism and unconscious bias that support nurses involved in the process.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".