Geographical variations in the correlates of blood donor turnout rates: An investigation of Canadian metropolitan areas
Bibliographic record
Abstract
BACKGROUND: Like other countries, Canada's population is aging, and the implications of this demographic change need to be better understood from the perspective of blood supply. Analysis of donor data will help to identify systematic patterns of donation and its correlates. DATA: Geo-coded blood donor and donor clinic data are provided by Canadian Blood Services. Blood donor data is provided for the fiscal year 2006-2007 indicating the total number of donors for each Canadian postal code, excluding the province of Québec. Potential correlates of blood donation are selected based on social and economic characteristics, as well as descriptors of city size and geographical location in the urban hierarchy measures of accessibility, and capacity of donor clinics. METHODS: Data is aggregated to n = 3,746 census tracts in 40 Census Metropolitan Areas (CMA) across the country. The number of donors per population in a census tract is regressed against the set of potential donation correlates. Autocorrelation is tested for and results adjusted to provide parsimonious models. RESULTS: A number of factors are found to influence donation across the country, including the proportion of younger residents, English ability, proportion of people with immigrant status, higher education, and a population-based measure of accessibility. CONCLUSION: While a number of correlates of blood donation are observed across Canada, important contextual effects across metropolitan areas are highlighted. The paper concludes by looking at policy options that are aimed toward further understanding donor behaviour.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".