Registration for deceased organ and tissue donation among Ontario immigrants: a population-based cross-sectional study
Bibliographic record
Abstract
<h3>Background:</h3> 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. <h3>Methods:</h3> 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. <h3>Results:</h3> Compared with long-term residents (<i>n</i> = 9 244 570), immigrants (<i>n</i> = 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 (<i>n</i> = 202 548), China (<i>n</i> = 186 678) and the Philippines (<i>n</i> = 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. <h3>Interpretation:</h3> 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".