Regional variation in the modeling of donation frequency: the case of Héma‐Québec, Canada
Bibliographic record
Abstract
BACKGROUND: Many studies on factors that can affect the frequency of blood donation have shown the influence of several individual characteristics. However, few studies have analyzed regional variations in blood donation frequency. The objective of this article is to verify to what extent individual and geographic variables influence blood donation in the Province of Québec, Canada. STUDY DESIGN AND METHODS: This article used a database provided by Héma-Québec (the organization in charge of blood collection in Québec), which included 426,247 donors, who made 1.4 million donations over a period of 5 years. Using the donors' residential postal codes and those of the blood collection sites, we created two geographic variables: the distance between the donor's place of residence and his or her collection site and each donor's region of residence. We subsequently modeled the frequency of blood donation and the different donor categories (based on the number of blood donations) using both a negative binomial regression model and an ordinal logistic regression model. RESULTS: The results indicate that, once the individual characteristics have been taken into account, the geographic variables, including proximity to the collection site, have a significant impact on the frequency of blood donation. Likewise, according to the results of the negative binomial model, among the 17 regions in the Province of Québec, there are five regions where blood donation incidence rate ratios (IRRs) are very high, that is, Abitibi-Témiscamingue (IRR, 1.77; 95% confidence interval [CI], 1.61-1.95); Bas-Saint-Laurent (IRR, 1.75; 95% CI, 1.59-1.93); Saguenay-Lac-Saint-Jean (IRR, 1.68; 95% CI, 1.53-1.84); Centre-du-Québec (IRR, 1.66; 95% CI, 1.51-1.83); and Chaudière-Appalaches (IRR, 1.62; 95% CI, 1.48-1.78). CONCLUSION: Such knowledge of the geography of blood donations makes it possible to better target certain regions when planning new blood drives, to ensure a constant blood supply.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".