First Nations Community Well-Being Research and Large Data Sets: A Respectful Caution

2017 ◽  
Vol 12 (2) ◽  
pp. 15-24 ◽  
Author(s):  
Alexandra S. Drawson ◽  
Aislin R. Mushquash ◽  
Christopher J. Mushquash
Keyword(s):  
First Nations ◽  
Social Determinants ◽  
Large Data ◽  
Well Being ◽  
Large Data Sets ◽  
Data Sets ◽  
Nations People ◽  
Level Data ◽  
Traditional Language ◽  
First Nations People

Health researchers are increasingly encouraged to use large, community-level data sets to examine factors that promote or diminish health, including social determinants. First Nations people in Canada experience disparity in a range of social determinants of health that result in relatively low community well-being scores, when compared to non-First Nations people. However, First Nations people also possess unique protective factors that enhance well-being, such as traditional language usage. Large data sets offer First Nations a new avenue for advocating for supports and services to decrease health inequity while developing culture-based evidence. However, care must be taken to ensure that these data are interpreted appropriately. In this paper, we respectfully offer a cautionary note on the importance of understanding culture and context when conducting First Nations health research with large data sets. We have framed this caution through a narrative presentation of a simple and concrete example. We then outline some approaches to research that can ensure appropriate development of research questions and interpretation of research findings.

Country Is Yarning to Me: Worldview, Health and Well-Being Amongst Australian First Nations People

2019 ◽  
Vol 50 (3) ◽  
pp. 944-960
Author(s):  
Mareese Terare ◽  
Margot Rawsthorne
Keyword(s):  
First Nations ◽  
Health Inequalities ◽  
Work Practice ◽  
Well Being ◽  
Social Work Practice ◽  
Social Determinant ◽  
Indigenous Australians ◽  
Creation Story ◽  
Nations People ◽  
First Nations People

Abstract Health inequalities experienced by Australian First Nations People are amongst the most marked in the world, with First Nations People dying some ten years earlier than non-Indigenous Australians. The failure of existing responses to health inequalities suggests new knowledges and questions that need to be explored. It is likely that these new knowledges sit outside of western research or practice paradigms. Through the Indigenous practice of yarning, the importance of worldview and Country emerged as an under-acknowledged social determinant of Australian First Nations People well-being. Yarning is a process of storytelling that involves both sound and silence. It requires embodied deep listening through which stories emerge that create new knowledge and understanding. We anchor our learning by re-telling John’s creation story, a story of healing through discovering his Aboriginal Worldview through reconnecting to Country. Country for First Nations People is more than a physical place; it is a place of belonging and a way of believing. We argue for the recognition of trauma, recognition of diversity and the use of yarning in social work practice. We conclude that reconnecting to Aboriginal Worldview provides hopeful insights into the well-being of Australia’s First Nations People and the social determinants of health.

Living standards in the past: new perspectives on well-being in Asia and Europe By Robert C. Allen, Tommy Bengtsson and Martin Dribe, eds. Oxford: Oxford University Press, 2005. Pp. xxii + 472. ISBN 0-19-928068-1

2006 ◽  
Vol 1 (2) ◽  
pp. 289-292 ◽  
Author(s):  
Anne Booth
Keyword(s):  
Labour Force ◽  
Large Data ◽  
Well Being ◽  
Living Standards ◽  
Large Data Sets ◽  
Data Sets ◽  
Household Surveys ◽  
Absolute Poverty ◽  
Oxford University ◽  
The Past

We live in an age of increasingly abundant statistical information. The advent of more large data sets obtained from household surveys, as well as from population censuses, labour force surveys, economic censuses and so on, has facilitated reasonably accurate estimates of income and expenditures for households in many parts of the world. These estimates can in turn be used to estimate a number of distributional indicators, as well as estimates of relative and absolute poverty. In addition better census coverage has permitted estimates of infant and child mortality rates, life expectancies, literacy rates and indicators of educational attainment. Such data have in turn been used to estimate composite indicators of wellbeing such as the Human Development Index, not just for entire countries but often for regions within countries as well.

Considering All of the Data on Digital-Media Use and Depressive Symptoms: Response to Ophir, Lipshits-Braziler, and Rosenberg (2020)

2020 ◽  
Vol 8 (2) ◽  
pp. 379-383
Author(s):  
Jean M. Twenge ◽  
Thomas E. Joiner ◽  
Megan L. Rogers ◽  
Gabrielle N. Martin
Keyword(s):  
Depressive Symptoms ◽  
Digital Media ◽  
Media Use ◽  
Large Data ◽  
Well Being ◽  
Large Data Sets ◽  
Data Sets ◽  
Individual Level ◽  
Monitoring The Future ◽  
The Individual

We have documented increases since 2012 in depressive symptoms, suicide-related outcomes, and suicide and identified associations between digital-media use and depressive symptoms and suicide-related outcomes across two data sets: Monitoring the Future (MtF) and the Youth Risk Behavior Surveillance System (YRBSS). Ophir, Lipshits-Braziler, and Rosenberg’s criticisms of the MtF data (this issue; pp. 374–378) are addressed by the YRBSS data, which included a measure of digital-media use in hours. Ophir et al. assumed that the displacement of nonscreen activities by screen activities occurs only at the individual level, whereas in fact, time displacement at the group or cohort level may be more important. Some discrepancies in the literature can be traced to the use of percentage variance explained; in fact, heavy (vs. light) digital-media users are considerably more likely (often twice as likely) to be depressed or low in well-being across several large data sets.

What Makes Us Strong: Urban Aboriginal Perspectives on Wellness and Strength

1997 ◽  
Vol 16 (2) ◽  
pp. 37-50 ◽  
Author(s):  
Collin P. Van Uchelen ◽  
Sara Florence Davidson ◽  
Seanna V. A. Quressette ◽  
Charles R. Brasfield ◽  
Lou H. Demerais
Keyword(s):  
Mental Health ◽  
First Nations ◽  
Aboriginal People ◽  
Health Planning ◽  
Well Being ◽  
Culturally Appropriate ◽  
Nations People ◽  
Downtown Eastside ◽  
Alternative Approach ◽  
First Nations People

The limitations of a needs orientation for aboriginal mental health planning are evaluated in terms of the discrepancy between First Nations and western medical paradigms of health. We propose an alternative approach that focuses on how aboriginal people conceptualize wellness and describe their strengths. This provides a focus for initiatives that promote well-being by enhancing strengths rather than concentrating solely on deficits. We illustrate this approach by highlighting the indigenous knowledge of urban First Nations people in Vancouver's Downtown Eastside neighbourhood. We conclude that supporting existing strengths promotes wellness in holistic, culturally appropriate, and empowering ways.

scholarly journals Local Population Studies in the Era of 'Big Data'

2019 ◽  
pp. 69-81
Author(s):  
Andrew Hinde
Keyword(s):  
Big Data ◽  
Population Studies ◽  
Local Level ◽  
Local Population ◽  
Large Data ◽  
Local History ◽  
Large Data Sets ◽  
Data Sets ◽  
Individual Level ◽  
Level Data

Recent years have seen a proliferation of very large data sets in historical demography, many of which have assembled individual-level data for entire populations. It might be thought that these data sets pose a challenge to local population studies, as they remove one of the rationales for work at the local level: that research using individual-level data was only logistically possible for small populations. This paper argues that, to the contrary, the advent of 'big data' sets provides new opportunities for work on local populations. Many of the old arguments in favour of local history still hold, but 'big data' can direct researchers to those places where local studies can potentially make the biggest contribution, and thus give local population studies a new lease of life.

scholarly journals The Preventing Dementia MOOC: Contribution to First Nations’ Health and Well-Being

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 11-11
Author(s):  
Lynette Goldberg ◽  
Dianne Baldock
Keyword(s):  
Risk Factors ◽  
First Nations ◽  
Aboriginal People ◽  
Well Being ◽  
Public Health Issue ◽  
Modifiable Risk Factors ◽  
Nations People ◽  
Increased Risk ◽  
First Nations People ◽  
Health And Well Being

Abstract Dementia is a global public health issue. First Nations people are at increased risk due to complex intergenerational factors grounded in inequalities in health services and economic and educational opportunities. While there is yet no drug-related cure for this progressive and terminal neurological condition, evidence confirms that increased understanding of dementia and modification of lifestyle factors can reduce risk. The primary potentially modifiable risk factors are not completing secondary school, midlife hypertension, obesity, type II diabetes, depression, physical inactivity, smoking, hearing loss acquired after the age of 55 years, and social isolation. Inherent in these factors is stress, affecting mental health. Addressing these factors globally could prevent or delay over 40 million cases of dementia. The free Preventing Dementia Massive Open Online Course (PD MOOC) is a globally recognized 4-week course that aims to build self-efficacy in knowledge and management of modifiable risk factors. The course has reached over 68,000 people world-wide and is rated highly; however, its contribution to First Nations communities has not yet been investigated. We describe the content of the PD MOOC, report on its impact in a cohort of older Aboriginal people (≥ 50 years of age) in Circular Head, Tasmania, Australia six months after course completion, and emphasize the importance of including traditional approaches to healing. We describe a protocol in which cultural determinants of health can be infused into the PD MOOC and evaluated to promote health and well-being globally for older First Nations people.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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