ArticleFrontiers in big data2019
Including Vulnerable Populations in the Assessment of Data From Vulnerable Populations.
Article in Frontiers in big data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- LGBTIQ CALD People's Experiences of Intimate Partner Violence: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- When genomics companies act as biobanks: governance challenges in an era of genomic assetization.Frontiers in genetics · 2026Article
- Beyond REMS & PDMPs: A Proposed Framework for Next-Generation Opioid Regulation.Therapeutic innovation & regulatory science · 2026Review
- Benchmark suites instead of leaderboards for evaluating AI fairness.Patterns (New York, N.Y.) · 2024Review
- Rethinking the frontiers of vaccine acceptance: Impact of visual messages in promoting the actual intake of COVID-19 vaccines among vulnerable populations.Human vaccines & immunotherapeutics · 2023Article
- Applying an evolutionary mismatch framework to understand disease susceptibility.PLoS biology · 2023Article
- Understanding COVID-19 Vaccine Hesitancy in Ethnic Minorities Groups in the UK.Frontiers in public health · 2022Article
- Article
- Expanded Ethical Principles for Research Partnership and Transdisciplinary Natural Resource Management Science.Environmental management · 2021Article
- How understudied populations have contributed to our understanding of Alzheimer's disease genetics.Brain : a journal of neurology · 2021Review
- Human genetic admixture.PLoS genetics · 2021Review
- Identifying adaptive alleles in the human genome: from selection mapping to functional validation.Human genetics · 2021Review
- So many Nigerians: why is Nigeria overrepresented as the ancestral genetic homeland of Legacy African North Americans?American journal of human genetics · 2021Article
- Don't blame the BAME: Ethnic and structural inequalities in susceptibilities to COVID-19.American journal of human biology : the official journal of the Human Biology Council · 2020Review
- Rights, interests and expectations: Indigenous perspectives on unrestricted access to genomic data.Nature reviews. Genetics · 2020Review
- Rewriting Human History and Empowering Indigenous Communities with Genome Editing Tools.Genes · 2020Article
- Seq-ing Higher Ground: Functional Investigation of Adaptive Variation Associated With High-Altitude Adaptation.Frontiers in genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data science has made great strides in harnessing the power of big data to improve human life across a broad spectrum of disciplines. Unfortunately this informational richesse is not equitably spread across human populations. Vulnerable populations remain both under-studied and under-consulted on the use of data derived from their communities. This lack of inclusion of vulnerable populations as data collectors, data analyzers and data beneficiaries significantly restrains the utility of big data applications that contribute to human well-ness. Here we present three case studies: (1) Describing a novel genomic dataset being developed with clinical and ethnographic insights in African Americans, (2) Demonstrating how a tutorial that enables data scientists from vulnerable populations to better understand criminal justice bias using the COMPAS dataset, and (3) investigating how Indigenous genomic diversity contributes to future biomedical interventions. These cases represent some of the outstanding challenges that big data science presents when addressing vulnerable populations as well as the innovative solutions that expanding science participation brings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.