Sociodemographic Variables in Canadian Organ Donation Organizations: A Health Information Survey
Bibliographic record
Abstract
Health systems must collect equity-relevant sociodemographic variables to measure and mitigate health inequities. The specific variables collected by organ donation organizations (ODOs) across Canada, variable definitions, and processes of the collection are not defined. We undertook a national health information survey of all ODOs in Canada. These results will inform the development of a standard national dataset of equity-relevant sociodemographic variables. Methods: We conducted an electronic, self-administered cross-sectional survey of all ODOs in Canada from November 2021 to January 2022. We targeted key knowledge holders familiar with the data collection processes within each Canadian ODO known to Canadian Blood Services. Categorical item responses are presented as numbers and proportions. Results: We achieved a 100% response rate from 10 Canadian ODOs. Most data were collected by organ donation coordinators. Only 2 of 10 ODOs reported using scripts explaining why sociodemographic data are being collected or incorporated training in cultural sensitivity for any given variable. A lack of cultural sensitivity training was endorsed by 50% of respondents as a barrier to the collection of sociodemographic variables by ODOs, whereas 40% of respondents identified a lack of training in sociodemographic variable collection as a significant barrier. Conclusions: Few programs routinely collect sufficient data to examine health inequities with an intersectional lens. Most data collection occurs midway through the ODO interaction, creating a missed opportunity to better understand differences in social identities of patients who register their intention to donate in advance or who decline the donation. National standardization of equity-relevant data collection definitions and processes of the collection is needed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".