Dataset Information

More Than Just Complaints: Generating Thick Engagement Through Thin Participatory Programs.

ABSTRACT: Thin participatory processes are designed for information exchange. But complaint processes, message boards, and hotlines sometimes facilitate thick, meaningful engagement. Can policy design leverage factors driving engagement to enable values-based responses to complex policy problems, while allowing information exchange to continue as-needed for most participants? Using place-based case studies from oil and gas drilling and community complaints in three regions of Colorado, I make propositions about how the drivers of collaboration are related to the practice of engagement within participatory programs. Based on the case studies, presence of coercive policy actors, inclusion of multiple governmental authorities, and transparency in participatory communication are likely to facilitate thickening. Meanwhile, measurement-based responses to participation are likely to propagate thin participation only. Accordingly, in designing thin processes, policy makers should recognize the nature of engagement will depend on the type of knowledge exchanged and the suite of institutions that might use or leverage the participatory action.

SUBMITTER: Scott RP

PROVIDER: S-EPMC6743688 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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More Than Just Complaints: Generating Thick Engagement Through Thin Participatory Programs.

Scott Ryan P RP

Perspectives on public management and governance 20190427 2

Thin participatory processes are designed for information exchange. But complaint processes, message boards, and hotlines sometimes facilitate thick, meaningful engagement. Can policy design leverage factors driving engagement to enable values-based responses to complex policy problems, while allowing information exchange to continue as-needed for most participants? Using place-based case studies from oil and gas drilling and community complaints in three regions of Colorado, I make propositions ...[more]

PMID: 31548995

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Project description:Objective:We learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints. Materials and methods:Retrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k?=?1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE). Results:The models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together. Discussion:Despite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space. Conclusion:Such a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

Dataset Information

More Than Just Complaints: Generating Thick Engagement Through Thin Participatory Programs.

Publications

More Than Just Complaints: Generating Thick Engagement Through Thin Participatory Programs.

Similar Datasets

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