DOI 10.1016/j.tracli.2025.08.001
Stratification of blood donors and understanding motivational and deterrent factors to develop effective recruitment strategies.
Аннотация
BACKGROUND AND
OBJECTIVES
Voluntary donation is vital for a safe and sufficient blood supply. This study aimed to use the cluster analysis to classify donors based on their demographics and to develop targeted strategies to enhance recruitment and retention.
METHODS
A prospective observational study was conducted over 1.5 years in a transfusion medicine center supporting a healthcare setup. Donor demographics and factors influencing blood donation were collected using a pre-validated proforma. Hierarchical cluster analysis, followed by two-step cluster analysis, was performed using six categorical variables: age category, gender, education, occupation, number of donations, and donation status. A dendrogram and agglomeration schedule were generated using squared Euclidean distance. Motivational and deterring factors were analyzed to develop strategies for promoting voluntary blood donation. Institutional ethics committee approval was obtained, and statistical analysis was performed using SPSS.
RESULTS
The study enrolled 13,594 voluntary blood donors, predominantly males (94 %) and young adults aged 18-39 years (80.6 %). The mean age was 31 years (SD: 9.4). Cluster analysis identified nine distinct clusters, with sizes ranging from 819 (6 %) to 2119 (15.6 %) donors. Positive emotions (47.1 %), personal reasons (28 %), and prosocial reasons (23.5 %) were primary motivators, while time constraints (28.7 %) and fear (25.8 %) were major deterrents. Cluster focused strategies for motivation and retention of voluntary blood donors were developed for nine different clusters.
CONCLUSION
The study effectively classified voluntary blood donors into nine distinct clusters, revealing unique patterns in demographics. Cluster-focused strategies formulated from the study offer a novel approach towards enhancing donor recruitment and retention.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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