Nova Scotia’s Deemed Consent for Deceased Organ Donation: Family Member Perspectives and Experiences in the ICU Setting
Bibliographic record
Abstract
Background: The purpose of this study was to explore the experience of family members of potential organ donors in the intensive care unit following the change to deemed consent legislation in Nova Scotia. Methods: This was a qualitative study with semistructured, in-depth interviews with 17 family members who were asked to make an organ donation decision on behalf of patients admitted to the intensive care unit in Nova Scotia. We analyzed themes using a descriptive approach. Participants were recruited from the organ donation organization in Nova Scotia, Canada. Results: Participant awareness and knowledge of the Human Organ and Tissue Donation Act legislation varied from individuals having no awareness and knowledge of the bill to those who had awareness and optimism that the legislation would be beneficial for increasing organ donation rates in the province. Other themes emerging from the interviews included (1) COVID context, (2) quality of healthcare professional care, (3) family support, and (4) barriers to donation (waiting, consent questionnaire, and patient transfers). Conclusions: The Human Organ and Tissue Donation Act legislation included enhanced support, which was viewed positively by family members. There is a need for continued evaluation as most participants felt it was too early to see the tangible impacts of the newly implemented legislation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".