Social workers’ critical role as service navigators on behalf of their clients is expanding in the online space at a faster pace than ever before. This study examined the process and outcome of online information navigation through the lens of service providers and service users based on observational and interactive surveys. T tests and correlation results showed that human services providers demonstrated a higher capacity to visit more websites and yield more accurate search outcomes in a similar duration of time compared with general service users. Results suggest that digital literacy for navigating information online can be improved through educational opportunities. At the same time, both groups shared some common feedback on desired features for future service navigation online, including but not limited to an open search bar, search filters, instruction videos, live chat, and discussion forums for seeking mutual help and networking. The findings bear implications for formulating the roles, responsibilities, and desired competencies of social workers for online service navigation in the digital and postpandemic future.
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Existing approaches for multi-label classification are trained offline, missing the opportunity to adapt to new data instances as they become available. To address this gap, an online multi-label classification method was proposed recently, to learn from data instances sequentially. In this work, we focus on multi-label classification tasks, in which the labels are organized in a hierarchy. We formulate online hierarchical multi-labeled classification as an online optimization task that jointly learns individual label predictors and a label threshold, and propose a novel hierarchy constraint to penalize predictions that are inconsistent with the label hierarchy structure. Experimental results on three benchmark datasets show that the proposed approach outperforms online multi-label classification methods, and achieves comparable to, or even better performance than offline hierarchical classification frameworks with respect to hierarchical evaluation metrics.more » « lessFree, publicly-accessible full text available December 15, 2024
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Machine learning models for hierarchical multilabel classification (HMC) typically achieve low accuracy. This is because such models need not only predict multiple labels for each data instance, but also ensure that predicted labels conform to a given hierarchical structure. Existing state-of the-art strategies for HMC decouple the learning process from ensuring that predicted labels reside in a path of the hierarchy, thus inevitably degrading the overall classification accuracy. To address this limitation, we propose a novel loss function, which enables a model to encode both a global perspective of the class hierarchy, as well local class-relationships in adjacent hierarchical levels, to ensure that predictions align with the class hierarchy, both during training and testing. We demonstrate the superiority of the proposed approach against multiple state-of-the-art methods for HMC on 20 real-world datasets.more » « lessFree, publicly-accessible full text available December 15, 2024
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Free, publicly-accessible full text available December 1, 2024