Registration for deceased organ and tissue donation among Ontario immigrants: a population-based cross-sectional study
Bibliographic record
Abstract
Background: Canada has low rates of deceased organ and tissue donation. Immigrants to Canada may differ in their registered support for deceased organ donation based on their country of origin. Methods: We used linked administrative databases in Ontario (about 11 million residents aged ≥ 16 yr) to study the proportion of immigrants and long-term residents registered for deceased organ and tissue donation as of October 2013. We used modified Poisson regression to identify and quantify predictors of donor registration. Results: Compared with long-term residents (n = 9 244 570), immigrants (n = 1 947 646) were much less likely to register for deceased organ and tissue donation (11.9% v. 26.5%). Immigrants from the United States, Australia and New Zealand had the highest registration rate (40.0%), whereas immigrants with the lowest registration rates were from Eastern Europe and Central Asia (9.4%), East Asia and Pacific (8.4%) and sub-Saharan Africa (7.9%). The largest numbers of unregistered immigrants were from India (n = 202 548), China (n = 186 678) and the Philippines (n = 125 686). Characteristics among the immigrant population associated with a higher likelihood of registration included economic immigrant status, living in a rural area (population < 10 000), living in an area with a lower ethnic concentration, less material deprivation, a higher education, ability to speak English and French, and more years residing in Canada. Interpretation: Immigrants in Ontario were less likely to register for deceased organ and tissue donation than long-term residents. There is a need to better understand reasons for lower registration rates among Canadian immigrants and to create culture-sensitive materials to build support for deceased organ and tissue donation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".