- Award ID(s):
- 1559546
- PAR ID:
- 10463475
- Date Published:
- Journal Name:
- Social Psychological and Personality Science
- ISSN:
- 1948-5506
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The donation of unharvested or unsold crops to rescue organizations has been promoted as a strategy to improve healthy food access for food insecure households while reducing production-level food loss and waste (FLW). In this study, we aimed to assess the motivations, barriers, and facilitators for crop donation as a FLW reduction strategy among Maryland farmers. We interviewed 18 Maryland-based food producers (nine frequent crop donors and nine infrequent, by self-report) in 2016 – 2017, soliciting their perspectives on crop donation motivators, process feasibility, and interventions aimed at increasing crop donation. The interviews were thematically coded. All respondents were aware of crop donation as an option, and most expressed interest in reducing FLW by diverting crop surpluses for human consumption. While financial barriers represented one aspect influencing donation decisions, respondents also cited convenience, process knowledge, and liability as key considerations. In contrast to frequent donors, many of whom considered donation a moral imperative, some infrequent donors questioned the expectation that they would donate crops without compensation. Both frequent and infrequent donors were aware of pro-donation tax incentives, and infrequent donors reported being unlikely to use them. This research demonstrates that crop donation motivations, barriers, and facilitators can be diverse. Given the existence of crop surpluses and their potential benefits as emergency food, our results suggest that multiple interventions and policies may contribute to incentivizing and facilitating crop donation (or enabling the purchase of surplus crops) rather than one-size-fits-all approaches. Our findings also highlight a need to prioritize crop recovery methods that enhance growers’ financial stability.more » « less
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Hunger and food insecurity are present in each American county. Government and non-government organizations are working to address food insecurity in the United States. Food banks are nonprofit hunger relief organizations that collect food and monetary donations from donors and distribute food to local agencies which serve people in need. Contributions come from retail donors, communities, and food manufacturers. The uncertainty of donation amounts and frequency is a challenge for food banks in the fight against hunger. In this research, we analyze local food bank donation data and propose a predictive model to forecast the contribution of different donors. Our study shows the necessary behavioral attributes to classify donors and the best way to cluster donor data to improve the prediction model. We also compare the accuracy of prediction for different conventional forecasting techniques with the proposed Support Vector Regression (SVR) model.more » « less
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Background Living kidney donation currently constitutes approximately a quarter of all kidney donations. There exist barriers that preclude prospective donors from donating, such as medical ineligibility and costs associated with donation. A better understanding of perceptions of and barriers to living donation could facilitate the development of effective policies, education opportunities, and outreach strategies and may lead to an increased number of living kidney donations. Prior research focused predominantly on perceptions and barriers among a small subset of individuals who had prior exposure to the donation process. The viewpoints of the general public have rarely been represented in prior research.
Objective The current study designed a web-scraping method and machine learning algorithms for collecting and classifying comments from a variety of online sources. The resultant data set was made available in the public domain to facilitate further investigation of this topic.
Methods We collected comments using Python-based web-scraping tools from the New York Times, YouTube, Twitter, and Reddit. We developed a set of guidelines for the creation of training data and manual classification of comments as either related to living organ donation or not. We then classified the remaining comments using deep learning.
Results A total of 203,219 unique comments were collected from the above sources. The deep neural network model had 84% accuracy in testing data. Further validation of predictions found an actual accuracy of 63%. The final database contained 11,027 comments classified as being related to living kidney donation.
Conclusions The current study lays the groundwork for more comprehensive analyses of perceptions, myths, and feelings about living kidney donation. Web-scraping and machine learning classifiers are effective methods to collect and examine opinions held by the general public on living kidney donation.
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