Understanding Public Perceptions of Deemed Consent Legislation for Organ Donation in Canada
Bibliographic record
Abstract
Background: Deemed consent legislation was implemented in Nova Scotia in 2021 and is being considered in other jurisdictions in Canada to increase deceased organ donation and transplantation rates. We have previously described the public perceptions of deemed consent and concerns for lawmakers to consider; however, there are other personal factors that should be understood prior to addressing these public concerns. Our objective was thus to explore the factors underlying public perceptions of deemed consent legislation in Canada. Methods: We searched four major Canadian news outlets for published articles about deemed consent between January 2019 and July 2020. Using a descriptive qualitative method, public comments from relevant articles were extracted and analyzed using an inductive conventional content analysis approach. Results: We extracted and analyzed 4,357 comments from 35 eligible news articles. Four primary themes emerged that helped explain the public's perception of deemed consent. Entwined beliefs: Commenters' beliefs included how they defined consent, human rights, and end-of-life practices. Connected experiences: Commenters described the impact of life experiences that were connected to deemed consent. These included being an organ donor or recipient but also other experiences such as accessing healthcare and government services (e.g., unable to navigate existing opt-in processes). Inescapable uncertainty: Organ donation was a difficult and personal decision. Many commenters were undecided in their decision to be an organ donor yet were being asked to firmly decide. Guarded trust: Some commenters described having little trust in healthcare providers, government officials, their family and friends, and others responsible for carrying out their wishes. Conclusions: Public perceptions of deemed consent appeared to be influenced by the collective value commenters ascribed to numerous related elements. Lawmakers should consider these determinants when addressing public concerns.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".