This dataset details the force-displacement response of porcine meniscus under tensile-fracture behavior. Samples are cut from the meniscus's anterior, middle, and posterior regions. Each specimen geometry dimension is included.
Background: Implicit racial bias (IB) in physicians contributes to racial health inequities. Residents are not consistently trained to address IB. Few curricula addressing IB in graduate medical education have been evaluated, especially in the clinical setting.
Objectives: The purpose of this study is to characterize Family Medicine (FM) residents’ experience of employing strategies to mitigate IB during primary care home visits (HVs) to urban, predominately African-American, homebound older adults using a phenomenological approach. The study outcomes will inform ongoing curriculum development.
Methods: FM residents completed pre-work, including taking the Implicit Association Test and evaluating strategies to address IB. Residents applied these strategies during HVs to homebound older adults. Residents completed written reflections about their experiences and commitments-to-change (CTC). A survey two months later assessed completion of targeted actions and barriers faced. Resident focus groups were utilized to enhance themes drawn from reflections. Researchers completed a thematic analysis of this data January-July 2020.
Results: Thematic analysis identified five themes: Response to IAT, barriers, strategies, value of HVs and mindfulness definition. In follow-up surveys, all residents’ stated level of CTC remained the same (9/9, 100%) and 8/9 residents (89%) had partially or fully implemented their intended change at 2 months.
Conclusions: Residents utilized the opportunity to learn and apply strategies to address IB. Residents continued implementing newly-learned strategies in the clinical setting two months after training and applied skills to settings outside of HVs and other bias types. These findings can facilitate development of meaningful, clinically-based IB curricula with lasting impacts.
CSV files containing the coherence scoring pertaining to datasets of:
DocumentCount = 5,000
Corpus = (one from) Federal Caselaw [cas] / Pubmed-Abstracts [pma] / Pubmed-Central [pmc] / Chicago Novel Corpus [nvl] / Newspaper Corpus [nws]
SearchTerm[s] = (one from) Earth / Environmental / Climate / Pollution / Random 5k documents of a specific corpus
Coherence was scored across every combination of:
TopicCount: 10-40
Hyperparameter-Alpha: [0.01, 0.31, 0.61, 0.91, symmetric, asymmetric]
Hyperparameter-Beta: [0.01, 0.31, 0.61, 0.91, automatic, symmetric]
The columns in this file include:
Validation_Set: Which search term this scoring pertains to
Topics: Number of topics in the model
Alpha: Hyperparameter alpha selection from the 6 options above
Beta: Hyperparameter beta selection from the 6 options above
Coherence: The topic coherence score for the given model-row
Perplexity: The perplexity score for the given model-row
Data and Text of Space in Media Coverage from including:
- 14,399 Documents of TV Transcripts (2010-2022)
- 9,061 documents of newspaper articles (2017-2022)
Betweenness centrality is a measure of centrality in a network based on shortest paths.
The data files in this collection are for datasets:
Document Count: 5,000 documents
Corpus: (one of) Caselaw (cas) / Pubmed Abstracts (pma) / Pubmed Central (pmc)
Search Term: (one of) Climate / Earth / Environmental / Pollution
Networked Models at Topic Counts: 15, 20