Attitudes and opinions on organ donation: an opportunity to educate in a Canadian city
Bibliographic record
Abstract
BACKGROUND: Patients on organ transplant waiting lists continue to far exceed donor rates. We seek to understand the barriers preventing people in a Canadian city from donating organs for transplantation. METHODS: One thousand adults were surveyed assessing knowledge, personal involvement, and attitudes about organ donation in an urban center in Canada. Primary outcomes of interest were a signed organ donor card and willingness to donate. RESULTS: Of those surveyed, 64% did not realize that they possessed an organ donor card; 90% would consider being a donor if a friend was in need of an organ. Of the 36% who did know, 72% had signed it. Those who had misconceptions about the organ donation process were less likely to be donors. INTERPRETATION: There is a tremendous lack of knowledge about organ donation. While the majority of people are interested in organ donation, they lack a means to express this interest - most do not even realize they possess an organ donor card. A significant proportion of people who were not supportive of donation were misinformed in critical areas of knowledge that likely influenced this decision including the rich being preferentially transplanted, the consent process, disfigurement, and donors receiving worse medical care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".