Blood donation clusters in <scp>Q</scp>uébec, <scp>C</scp>anada (2003–2008): spatial variations according to sex and age
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The detection of spatial clusters of blood donation rate is an important issue, especially for targeting spatial units with significantly low rates, where it could be possible to increase the numbers of donors. The objective of this study is to detect spatial clusters of high or low blood donation rate in Québec according to sex and age of the donors. MATERIALS AND METHODS: Blood donation data were obtained from Héma-Québec over a period of 5 years. We aggregated these data for each of 101 municipalités regionales de comté (i.e. counties) for men, women and four age groups. To detect spatial high/low donation rate areas, we used the Kulldorff's scan statistics. Kappa coefficient was used to assess discordance between clusters obtained for the different groups (18-29, 30-39, 40-49, 50-59, 60-69 years old). T-test analyses were conducted to identify significant associations between spatial clusters and socio-economic variables. RESULTS: The results indicate the presence of several geographical areas with high or low blood donation rates for each group. The size, the location and the socio-demographic profiles of low/high clusters vary according to sex and age categories. CONCLUSION: The Kulldorff's scan statistics are an efficient tool to assess the blood donation performance across a country or even a specific region over a period of several years. In terms of strategic planning and monitoring, it can be used as a fully operational tool to target areas with significantly low rates (for all donors or specific demographic groups) in future blood donation campaigns.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".