Blood donor return behavior in South Africa and the United States before and during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Studies preceding the COVID-19 pandemic found that slower time-to-return was associated with first-time, deferred, and mobile drive blood donors. How donor return dynamics changed during the COVID-19 pandemic is not well understood. METHODS: We analyzed visits by whole blood donors from 2017 to 2022 in South Africa (SA) and the United States (US) stratified by mobile and fixed environment, first-time and repeat donor status, and pre-COVID19 (before March 2020) and intra-COVID19. We used Kaplan-Meier curves to characterize time-to-return, cumulative incidence functions to analyze switching between donation environments, and Cox proportional hazards models to analyze factors influencing time-to-return. RESULTS: Overall time-to-return was shorter in SA. Pre-COVID19, the proportion of donors returning within a year of becoming eligible was lower for deferred donors in both countries regardless of donation environment and deferral type. Intra-COVID19, the gap between deferred and non-deferred donors widened in the US but narrowed in SA, where efforts to schedule return visits from deferred donors were intensified, particularly for non-hemoglobin-related deferrals. Intra-COVID19, the proportion of donors returning within a year in SA was higher for deferred first-time donors (>81%) than for successful first-time donors (80% at fixed sites; 69% at mobile drives). CONCLUSIONS: The pandemic complicated efforts to recruit new donors and schedule returning visits after completed donations. Concerted efforts to improve time-to-return for deferred donors helped mitigate donation loss in SA during the public health emergency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".