Journals | Policy | Permission

Journal of Neurology Research

Review

Volume 12, Number 2, August 2021, pages 54-68

Auditing the Impact of Neuro-Advancements on Health Equity

“Health equity means that everyone has a fair and just opportunity to be as healthy as possible” and “reducing and ultimately eliminating disparities in health and its determinants that adversely affect excluded or marginalized groups” [1]. As such, health equity is an important aspect of a good life [2, 3] and has many social determinants [4]. Various tools exist to analyze the social of health and well-being, such as social determinants of health (SDH), OECD Better Life Index (BLI), Canadian Index of Well-Being (CIWB), community-based rehabilitation (CBR) matrix, WHOQoL, The Quality of Being Scale, Aqol, and Calvert-Henderson Quality of Life Indicators, Satisfaction With Life Scale, Perceived Life Satisfaction Scale, Flourishing Scale, Scale of Positive and Negative Experience, Comprehensive Inventory of Thriving, Brief Inventory of Thriving, “The Disability and Wellbeing Monitoring Framework and Indicators”, and the capability approach (from now on called “the measures”) [5-31]. Many indicators of the “measures” are impacted by neuroscientific and neurotechnological advancements and the “measures” could be useful audit tools to evaluate the impact of neuroscientific and neurotechnological advancements on the ability of people and groups to experience health equity, which is one facet of the ability to have a good life. In this study, I ask three research questions covering over 50 neurotechnologies, neuroenhancement, neuroethics, neurotechnology governance, artificial intelligence (AI) machine learning (ML), and robotics and “the measures”: (1) Are the “measures” engaged with in the academic literature covering health equity or the chosen technologies? (2) Does the academic literature focusing on the technologies covered, neuroethics, or neurotechnology governance engage with health equity? (3) To what extent does the academic literature focusing on the technologies covered engage with the different primary and secondary indicators of four of the “measures” (SDH, BLI, CIWB, and CBR matrix)? To answer the research questions, I performed quantitative hit counts searches on the co-occurance in academic abstracts of 1) the terms or phrases “robot*”, “robotic*”, “artificial intelligence”, “machine learning”, 50 terms and phrases linked to neurotechnologies and terms depicting neuro-related human enhancement and the “measures” [5-31]; 2) the technologies mentioned or the terms “neuroethic*”, “neuro-governance”, “neurotechnology governance” and the phrase “ health equity”; and 3) the primary and secondary indicators of four of the “measures” (SDH, Canadian and other interpretations, CIWB, the World Health Organization (WHO) CBR matrix and the OECD BLI) and the technologies mentioned. The findings are discussed through the premises of the “measures” and the purpose of science and technology governance.

It is noted that the academic literature increasingly engages with the concept of health equity [32]. Although there is a definition of health equity that equals health equity only with equal access to healthcare services [33], my article follows the WHO definition [34] and others [1-3, 35-37] that have a broader view on health equity.

The WHO definition is: “the absence of unfair and avoidable or remediable differences in health among population groups defined socially, economically, demographically or geographically. In essence, health inequities are health differences that are socially produced, systematic in their distribution across the population, and unfair. Identifying a health difference as inequitable is not an objective description, but necessarily implies an appeal to ethical norms” [34].

One other health equity definition following the WHO scope is “health equity is about the “freedom to live a long and healthy life” and “the material, psychosocial and political empowerment of individuals and communities” whereby “these dimensions of empowerment are influenced by the way society chooses to run its affairs, which shape the conditions in which people are born, live, work, play and age. Daily living conditions affect peoples’ opportunities, their chances, the ways they behave and feel, and ultimately their health” [3]. And a third one states “health equity is about social exclusion” [1].

To achieve health equity one has to remove disadvantages based on one’s social position [2, 36] and any other socially defined circumstance [36] that especially impact in a negative way socially marginalized groups [1] such as poverty, discrimination, lack of power and lack of access to meaningful jobs, education, housing, a safe environments, and health care [35].

It is argued that health equity has to include “intergenerational planetary health equity” [3], intersectionality [1], and an engagement with the SDH [4]. Organizational climate and capacity are important for health equity-oriented practice [38] and “significant gaps have been identified in the integration of health equity and social justice into health professions education programs in Canada across all educational levels” [39]. Measures for health equity have been proposed [1, 2, 40].

The purpose of the WHO CBR [16] is to enhance the quality of life, to equalize the opportunities and to increase the social inclusion of people with disabilities and people linked to them [17, 41] in low- and middle-income countries. The CBR guidelines have been developed by over “180 individuals and representatives of nearly 300 organizations, mostly from low-income and middle-income countries”[42]. The CBR is seen to adhere “to the principles of the Convention on the Rights of Persons with Disabilities (CRPD), e.g. non-discrimination and the need to include all people with disabilities in development initiatives” [42] and to protect the rights of people with disabilities [41].

There are indexes of well-being in various countries [11-14, 43], including Canada [9, 10] and there are efforts to develop a global index of well-being [43] with the OECD BLI [15] being classified as an index of well-being [43]. One group came out in April 2020 with “The Disability and Wellbeing Monitoring Framework and Indicators” [27, 28], which covers over 193 indicators linked to health and well-being. They map the 193 indicators to the rights articulated in the CRPD [44] and domains from the International Classification of Functioning, Disability and Health (ICF) [45] to their CRE-DH Monitoring Framework domains [27]. They acknowledge that “the vision of the WHO Global Disability Action Plan 2014-2021 is a world in which all persons with disabilities and their families live in dignity, with equal rights and opportunities, and are able to achieve their full potential” and that “people with disability are more likely than those without to experience poverty, violence, social exclusion, housing insecurity, unemployment and economic inactivity” [27]. The CIWB [9, 10] has as its definition of well-being: “The presence of the highest possible quality of life in its full breadth of expression focused on but not necessarily exclusive to: good living standards, robust health, a sustainable environment, vital communities, an educated populace, balanced time use, high levels of democratic participation, and access to and participation in leisure and culture” [9]. Then there are many different quality of life measures such as WHOQoL [18], The Quality of Being Scale [31], Aqol [20] , and Calvert-Henderson Quality of Life [21], Satisfaction With Life Scale [22-24], Perceived Life Satisfaction Scale [25, 26], Flourishing Scale [29], Scale of Positive and Negative Experience [29], Comprehensive Inventory of Thriving [30], and Brief Inventory of Thriving [30]. All of the indicators of these well-being measures can be seen to be indicators of health equity and the ability to have a good life.

“The SDH [46-48] are the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life” [49]. According to “Social Determinants of Health: The Canadian Facts”, SDH are about “Canadian society, and what we need to put faces and voices to the inequities, and the health inequities in particular, that exist in our midst. Only when we see a concrete description of these complex and challenging problems, when we read about their various expressions in all the regions of the country and among the many sub-groups making up Canada, can we move to action” [47].

“The scope of neuro-advancements ranges from medical/therapeutic, military, education, human enhancement to recreational” [50]. Neuroethics [51-58] and neuro-governance [59] emerged as two fields covering the social, legal, and ethical issues that arise with neuro-advancements [51-54, 58-64], and it is argued that the societal discussions of science and technology advancements which would include neuro-advancements should be a constant endeavor [65, 66]. AI including ML and neuro-advancements increasingly intersect [59, 67-77], as do neuro-advancement such as brain-computer interface (BCI) and robotics [78-83] and neuro-linked enhancement beyond the species-typical [84, 85]. An extensive body of literature on the potential implications and governance of advancements exists for AI [86-92] and the ethics and governance of AI and neuro-discussions increasingly intersect [93-97].

Scoping studies are useful in identifying the extent of research that has been conducted on a given topic [98, 99] and the current understanding of a given topic. In this case, I aimed to record the co-occurance in academic abstracts of 1) the terms or phrases “robot*”, “robotic*”, “artificial intelligence”, “machine learning”, 50 terms and phrases linked to neurotechnologies and terms depicting neuro-related human enhancement and the “measures” [5-31]; 2) the technologies mentioned or the terms “neuroethic*”, “neuro-governance”, “neurotechnology governance” and the phrase “ health equity”; and 3) the primary and secondary indicators of four of the “measures” (SDH, Canadian and other interpretations, CIWB, WHO CBR matrix and the OECD BLI) and the technologies mentioned. Three research questions were investigated: (1) Are the “measures” engaged with in the academic literature covering health equity or the chosen technologies? (2) Does the academic literature focusing on the technologies covered, neuroethics, or neurotechnology governance engage with health equity? (3) To what extent does the academic literature focusing on the technologies covered engage with the different primary and secondary indicators of four of the “measures” (SDH, BLI, CIWB, and CBR matrix)?

On September 28, 2020 (strategy 1 and 2), and June 14, 2021 (strategy 3), the academic databases EBSCO-HOST (an umbrella database that includes over 70 other databases itself) and SCOPUS (which incorporates the full Medline database collection) were searched with no time restrictions. These databases contain journals that cover a wide range of topics from areas of relevance to answer the research questions. Scholarly, peer-reviewed journals were included in the EBSCO-HOST search and reviews, peer-reviewed articles, conference papers, and editorials in the SCOPUS search.

For strategy 1c, 2c, and 3c, 50 neurotechnology-related terms were covered: (“artificial brain”) OR (“artificial hippocampus”) OR (“auditory brainstem implant”) OR (“Bionic eye”) OR (“Brain Computer Interface”) OR (“brain feedback”) OR (“brain imaging”) OR (“brain stimulation”) OR (“Brain to speech technology”) OR (“Brain-to-text technology”) OR (“Cochlear implant”) OR (“cognitive imaging”) OR (“cognitive stimulation”) OR (“Collaborative cognitive simulations”) OR (“CoriQ electrocorticographic”) OR (“Cortical modem”) OR (“cranial electrotherapy stimulation”) OR (“Darpa Ram senor”) OR (“deep brain stimulation”) OR (“Direct Acoustic Cochlear Implant”) OR (“Ear-EEG”) OR (“EEG biofeedback”) OR (“Electrocorticography”) OR (“exocortex”) OR (“Facial Electromyography”) OR (“God Helmet”) OR (“Hemoencephalography”) OR (“Hippocampus prosthesis”) OR (“human computer Interface”) OR (“intracranial electroencephalography”) OR (“Muse headband”) OR (“Neural stem cell”) OR (“Neuralink”) OR (“Neuro-chip”) OR (“neuro-information”) OR (“neuro-modulation”) OR (“neurofeedback”) OR (“neuroimaging”) OR (“neurosensing”) OR (“neurostimulation”) OR (“Nootropics”) OR (“Optogenetics”) OR (“Prosthetic memory device”) OR (“Pulsed electromagnetic field therapy”) OR (“Responsive neurostimulation”) OR (“Sacral nerve stimulation”) OR (“Speech brain computer interface”) OR (“Spinal cord stimulator”) OR (“Subvocal speech device”) OR (“subvocal”) OR (“THync mood altering headset”) OR (“transcranial direct current stimulation”) OR (“transcranial magnetic stimulation”) OR (“virtual reality”) OR (“whole brain emulation”).

To answer the research questions, a descriptive quantitative analysis approach [100, 101] was performed generating hit counts for the search term combinations of the strategies (Table 1).

Click to view

Table 1. Search Strategies Used to Obtain Quantitative Hit Counts

The search was limited to databases accessible through EBSCO-HOST (70 databases) and SCOPUS and English language literature. As such, the findings are not to be generalized to the whole academic literature, non-academic literature, or non-English literature. As the data produced are based on the co-occurrence of terms, the hit counts by themselves do not indicate whether the keyword combinations really engage content wise with each other or what the actual content is. The numbers are also very likely lower as I did not eliminate duplicates of results due to articles showing up in more than one database. This could be done in follow-up studies for any of the hit counts reported by authors who want to focus for example on specific indicators. The findings also cover only specific terms depicting specific areas of scientific and technological advancements. Therefore, our findings cannot be generalized to every scientific or technological advancement. These findings, however, allow conclusions to be made within the parameters of the searches and the character of the analysis. The method section gives all the details needed to perform modified studies using for example other technologies and other databases.

In this section, I provide first hit count results for the presence of the term “health equity” in conjunction with robot-related terms, artificial intelligence, machine learning, 50 neurotechnologies and neuro-related enhancements (from now on called “technologies covered”) or in conjunction with the terms “neuroethics*”, “neurotechnology governance” or “neuro-governance” (Table 2), then the hit counts for technologies covered and the term “health equity” in relation to the measures (Table 3), and finally the hit counts of the technologies covered in conjunction with the indicators of the four of the “measures” (SDH, Canadian and other interpretations, CIWB, WHO CBR matrix and the OECD BLI) (Tables 4-7).

Click to view

Table 2. Hit Counts for the Term Health Equity in Conjunction With Technologies and Neuroethics and Neurotechnology Governance

Click to view

Table 3. Hit Counts for the Terms Used for the Various “Measures” in Conjunction With Technologies Covered or Health Equity

Click to view

Table 4. Presence of Community-Based Rehabilitation Matrix Indicators in Conjunction With Technologies Covered

Click to view

Table 5. Presence of Canadian Index of Wellbeing Indicators in Conjunction With Technologies Covered

Click to view

Table 6. Presence of Better Life Index Indicators in Conjunction With Technologies Covered

Click to view

Table 7. Presence of Social Determinants of Health (SDH) Indicators in Conjunction With the Technologies Covered

In Table 2, I report the hit counts obtained for health equity in conjunction with the technologies covered and the terms “neuroethics*”, “neurotechnology governance” or “neuro-governance”.

Table 2 highlights that health equity is rarely engaged with, in relation to the technologies covered and not at all in relation to neuroenhancements, neuroethics and neuro-governance.

In Table 3, I report the hit counts obtained with the names of the “measures” and health equity and the measures and the technologies covered.

Table 3 shows that the terms linked to the “measures” rarely to not at all co-occur with the technologies covered and only the terms “determinants of health” and “social determinants of health” show substantial co-occurrence with the term “health equity”.

In Tables 4-7, I report on the co-occurrence of the indicators of four of the measures with the technologies covered.

Tables 4-7 suggest an uneven engagement with many of the indicators suggesting that areas that could be impacted by the technologies were rarely covered or not covered at all. Furthermore, even if terms such as “discrimination” had quite a few hits, the lack of hits for other terms such as “women with disabilities” or few hits with terms depicting indigenous people the question arises who is engaged with under the term “discrimination”.

As to the “measures”, the scoping review found that the term “health equity” was only mentioned in a substantial way in conjunction with the “measures” related terms “determinants of health” and “social determinants of health”. Three of the “measures” related terms, “capability approach”, “well-being index” and “meaning in life”, were mentioned in less than 10 sources. The other 16 “measures” related terms were not mentioned in conjunction with the term “health equity”. The term “health equity” was also rarely mentioned in relation to robotics (three times), artificial intelligence, and machine learning (13 times) and the 50 neurotechnologies (three times) and not at all in conjunction with neuro-related enhancements and the terms “neuroethics”, “neuro-governance” and “neurotechnology governance”. Of the “measures” [5-31], only the terms “social determinants of health” and “determinants of health” were mentioned in a substantial way in conjunction with the technologies investigated. Furthermore, there was uneven engagement with the indicators of the four “measures” investigated as examples (SDH, BLI, CIWB, and CBR matrix) in relation to the technologies covered. These findings are problematic given that it is acknowledged for all the technologies covered that they have social implications as evident by the very existence of the fields of neuroethics, robo-ethics and AI ethics. It also highlights vast opportunities. The findings are discussed through the premises of the “measures” and the purpose of science and technology governance.

All the “measures” [5-31] exist to analyze health, well-being, and quality of life of people and their indicators could be labelled as indicators of health equity. Various, academic articles make the linkage between the indicators of the “measure” (SDH) and health equity [3, 4, 102, 103]. Nearly all, if not all, indicators of these “measures” are impacted by the technologies covered in the study whether directly or indirectly. For example, it has been reported that social status is a driver for human enhancement [104], and marginalized groups have a social status problem. However, the term “social status” was rarely found. As such, all the “measures” covered by the study could and should be used to audit the impact of the technologies covered on health equity and the social in general. To look now at four of the measures, I investigated in more detail in the study.

The CBR measure and the CBR matrix [42] have been developed by groups and organizations from middle- and low-income countries for its use in making the lives of people with disabilities better in low- and middle-income countries [42]. The technologies covered in my study are also envisioned to be deployed in low- and middle-income countries [105-108] and therefore these technologies will impact the social and health equity in these countries especially of marginalized groups [105]. As such the CBR matrix and its indicators could and should be used at least by the CBR community to audit the social impact of the technologies covered. The CBR is about creating equal opportunities [41]. Health equity is about the equal opportunity for being healthy [109]. It is furthermore noted that the CBR is guided by the CRPD [42, 110] and many of the problems faced by disabled people that are flagged in the CRPD [44] are impacted by the technologies covered [84, 85, 105, 111-114]. Therefore, the CBR matrix could and should be used as a lens to investigate the impact of the technologies covered on the creation of equal opportunities to be healthy for disabled people and beyond and to audit the impact of the technologies covered on the problems flagged in the CRPD. The CBR matrix is to my knowledge the only measure that explicitly lists Disabled People’s Organizations as an indicator. As such, the CBR community has a unique indicator that one can apply to audit the impact of technologies on health equity. Finally, empowerment of disabled person is one indicator of the matrix [41]. As such, the CBR matrix could and should be used to highlight the dangers and opportunities of the technologies covered in this study for the empowerment of disabled people and others.

“SDH” are the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life” [49] and with that health equity [2, 4, 115, 116]. All the technologies covered impact the very conditions of daily life and with that health equity [49]. The final report of the “Commission for the social determinants of health” outlined three main action items: 1) “Improve the conditions of daily life - the circumstances in which people are born, grow, live, work, and age; 2) Tackle the inequitable distribution of power, money, and resources - the structural drivers of those conditions of daily life - globally, nationally, and locally; and 3) Measure the problem, evaluate action, expand the knowledge base, develop a workforce that is trained in the social determinants of health, and raise public awareness about the social determinants of health” [8]. All three points suggest that the SDH could and should be used to audit and evaluate the impact of science and technology including the technologies covered on health equity and the social in general.

The CIWB is seen as “a multifaceted measurement and monitoring tool developed to engage Canadians in conversations about their health and well-being that go beyond health care or the economy, and about acting on changes that matter in their lives” [117]. As such, this measure could and should be used to audit advancements in science and technology in general and the technologies covered. The same conclusion can be reached in relation to the other quality of life measures covered under the “measures” (WHOQoL [18], The Quality of Being Scale [31], Aqol [20], Calvert-Henderson Quality of Life [21], Satisfaction With Life Scale [22-24], Perceived Life Satisfaction Scale [25, 26] Flourishing Scale [29], Scale of Positive and Negative Experience [29], Comprehensive Inventory of Thriving [30], and Brief Inventory of Thriving [30]).

Recently, people highlighted the 2017 UNESCO Recommendation on Science and Scientific Researchers as a tool that, if followed, would “transform working conditions, rights and responsibilities of researchers globally” as part of performing responsible research and innovation [118]. The 10 main points stated are: 1) Responsibility of science towards the United Nations’ ideals of human dignity, progress, justice, peace, welfare of humankind and respect for the environment. 2) Need for science to meaningfully interact with society and vice versa. 3) Role of science in national policy and decision-making, international cooperation and development. 4) Promotion of science as a common good. 5) Inclusive and non-discriminatory work conditions and access to education and employment in science. 6) Any scientific conduct is subject to universal human rights standards. 7) Balancing the freedoms, rights and responsibilities of researchers. 8) Scientific integrity and ethical codes of conduct for science and research and their technical applications. 9) Importance of human capital for a sound and responsible science system. 10) Role of Member States in creating an enabling environment for science and research [118].

Auditing the “social” including health equity impact of science and technology advancements in general and the technologies covered in a cohesive and systematic fashion is essential for at least points 1-4. It is noted that “biases in science” are one topic covered in the health equity literature [32]. These “measures” could unmask biases as to how the social and health equity is engaged with in relation to the technologies covered and other scientific and technological advancements.

In the report of a recent workshop “The Endless Frontier: The Next 75 Years in Science” organized by the National Academy of Sciences, The Kavli Foundation and the Alfred P. Sloan Foundation [119] mentioned the term “social” 13 times, “society” 36 times, and “societal” nine times. It is furthermore stated in the report that “World War II demonstrated that science was becoming the single most important force driving technological, economic, and societal change” [119] and “today, society is counting on science, engineering, and medicine to respond to and recover from the COVID-19 pandemic, an unprecedented health, economic, and social crisis” [119]. The report also argues that future challenges impact societal inequities and societal polarizations [119] and concludes “The result has been an increasingly international, collaborative, and interdisciplinary science and technology system that has become an even greater force for economic growth and social change than in the postwar years” [119]. If all these statements in the report are true, the arguments made also impact health equity, and as such, the technologies covered should engage more with health equity. According to Rafael Reif, President, Massachusetts Institute of Technology, it would be useful to have “a new technology directorate at NSF focused on high-stakes technologies, with the humanities and social sciences integral to the research from the start because of these technologies’ social consequences” [119]. The “measures” could be used as a tool to bring people from various backgrounds together and decrease silos benefiting health equity endeavors.

“Health equity means that everyone has a fair and just opportunity to be as healthy as possible” and “reducing and ultimately eliminating disparities in health and its determinants that adversely affect excluded or marginalized groups” [1]. As such, health equity is an important aspect of a good life [2, 3] and has many social determinants [4]. Neuroscientific and neurotechnological advancements and the technologies that enable such advancements impact various indicators of these social determinants as do other scientific and technological advancements. The “measures” [5-31] could be used to audit the impact of the technologies on health equity and the social in general, which would help endeavors such as “integrating health equity and racial justice into the artificial intelligence” [120]. The “Disability and Wellbeing Monitoring Framework and Indicators” together with scales such as the Satisfaction With Life Scale [22-24] and Perceived Life Satisfaction Scale [25, 26] could be a good starting point to gain an understanding of the perception and realities (real or anticipated) of a given science and technology advancement such as the technologies covered in this study on the “social” parameters influencing health equity of disabled people.

Buchman and Wadwahan argued that neuroethics should engage “more with theories of social justice, particularly how neurotechnologies might “affect already unequal societies” [121], which they argue includes “analyses that are rooted in health equity and the social determinants of health” [121]. This study contributes to this analysis by mapping out what is covered and not using a hit count analysis. The findings of the study provide data showing that there is a lack of linkage between neuro-advancements and health equity and no linkage at all between neuroethics and neuro-governance and health equity in the academic literature covered, which reinforces the demand by Buchman and Wadwahan and suggests also that there should be more of a focus on health equity in neuroethics and neuro-governance discussions.

All the findings of the study suggest vast opportunities for the fields covering the “measures” and health equity, science and technology governance, neuroethics, AI ethics, robo-ethics, the development of the technologies covered, and other communities such as disability studies and other identity group studies, social justice studies, socially disadvantaged groups, practitioners, and policy makers to name a few groups to collaborate in an intersectional and interdisciplinary fashion.

The findings of the study suggest many possible future research agendas. One is that one can use all the indicators of the “measures” and ask various groups and individuals, including disabled people, which of the indicators they experience in the moment in a positive or negative way and how they see a given scientific or technological advancement impacting how they experience the indicators now and in the future. One can investigate why there is this disconnect between these “measures” as a whole, science and technology governance, and health equity as a concept, and further, how the “measures” can be used in curricula to increase the literacy of students on the impact of the technologies covered on health equity and the social in general. One can also use these “measures” to advance the “equity, diversity and inclusion” (EDI) agenda at universities as many of the EDI groups are negatively impacted by the social environment they experience outside universities [122, 123], which impacts their enrolment and advancements in universities. Being literate on the impact of the technologies covered and others on health equity and the social in general enables more literate EDI and other discourse in universities.

Acknowledgments

None to declare.

Financial Disclosure

This research was funded by Government of Canada, Canadian Institutes of Health Research, Institute of Neurosciences, Mental Health and Addiction ERN 155204 in cooperation with ERA-NET NEURON JTC 2017.

Conflict of Interest

None to declare.

Data Availability

The author declares that data supporting the findings of this study are available within the article.

1. Zimmerman FJ. A robust health equity metric. Public Health. 2019;175:68-78.
   doi pubmed
2. Braveman P, Gruskin S. Defining equity in health. J Epidemiol Community Health. 2003;57(4):254-258.
   doi pubmed
3. Backholer K, Baum F, Finlay S. Australia in 2030. What is our path to health for all? online: Medical Journal of Australia. 2021. Available from: https://www.mja.com.au/system/files/2021-05/MJA%20supplement_214_8_3%20May.pdf.
4. Deferio JJ, Breitinger S, Khullar D, Sheth A, Pathak J. Social determinants of health in mental health care and research: a case for greater inclusion. J Am Med Inform Assoc. 2019;26(8-9):895-899.
   doi pubmed
5. World Health Organization. Constitution of the World Health Organization online: World Health Organization. 1948. Available from: https://www.who.int/governance/eb/who_constitution_en.pdf.
6. Marmot M, Friel S, Bell R, Houweling TA, Taylor S, Commission on Social Determinants of H. Closing the gap in a generation: health equity through action on the social determinants of health. Lancet. 2008;372(9650):1661-1669.
   doi pubmed
7. Raphael D. Social determinants of health: Canadian perspectives: Canadian Scholars’ Press. 2009.
8. Commission on Determinants of Health. Closing the gap in a generation: Health equity through action on the social determinants of health online: World Health Organization. 2006. Available from: https://www.who.int/social_determinants/thecommission/finalreport/en/.
9. Canadian Index of Wellbeing Organization. What is Wellbeing? online: Canadian Index of Wellbeing. 2019. Available from: https://uwaterloo.ca/canadian-index-wellbeing/what-wellbeing.
10. Michalos AC, Sharpe A, Muhajarine N. An approach to the Canadian index of wellbeing. Toronto: Atkinson Charitable Foundation. 2010.
11. Tanton R, Miranti R, Vidyattama Y, Sajeda T. Index of wellbeing of older Australians (IWOA). 2019.
12. Stanojevic A, Bencina J. The construction of an integrated and transparent index of wellbeing. Soc Indic Res. 2019;143;995-1015.
    doi
13. Linares-Lujan AM, Parejo-Moruno FM. Height, literacy and survival: a composite index of wellbeing based on data from military recruitment (1880-1980). Soc Indic Res. 2019;144:999-1019.
    doi
14. Atkinson S, Bagnall A-M, Corcoran R, South J, Curtis S. Being well together: individual subjective and community wellbeing. Journal of Happiness Studies. 2020;21:1903-1921.
    doi
15. OECD. OECD Better Life Index. 2020. Available from: http://www.oecdbetterlifeindex.org/#/11111111111.
16. World Health Organization. About the community-based rehabilitation (CBR) matrix: World Health Organization. 2011. Available from: http://www.who.int/disabilities/cbr/matrix/en/.
17. World Health Organization. Community-based rehabilitation: World Health Organization; 2016. Available from: http://www.who.int/disabilities/cbr/en/.
18. The WHOQOL Group. The World Health Organization Quality of Life assessment (WHOQOL): position paper from the World Health Organization. Soc Sci Med. 1995;41(10):1403-1409.
    doi pubmed
19. Wolbring G. HTA Initiative: The triangle of enhancement medicine, disabled people, and the concept of health: a new challenge for HTA, health research, and health policy. Alberta Heritage Foundation for Medical Research (AHFMR) webpage: Alberta Heritage Foundation for Medical Research (AHFMR). 2005. Available from: http://www.ihe.ca/download/the_triangle_of_enhancement_medicine_disabled_people_and_the_concept_of_health_a_new_challenge_for_hta_health_research_and_health_policy.pdf.
20. Hawthorne G, Richardson J, Osborne R. The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of health-related quality of life. Qual Life Res. 1999;8(3):209-224.
    doi pubmed
21. Flynn P, Lickerman J, Henderson H. Calvert-Henderson quality of life indicators. Calvert Group: Bethesda, MD. 2000.
22. Diener E. Subjective well-being. Psychol Bull. 1984;95(3):542-575.
    pubmed
23. Mulderrig K, Rogers S. Deaf cultural identification, cochlear implants, and life satisfaction. Canadian Acoustics - Acoustique Canadienne. 2019;47(3):20-21.
24. Wolbring G. Subjective wellbeing, body-related ability expectations and peace. International Journal of Peace Studies. 2013;18(2):27-39.
25. Huebner ES, Dew T. Validity of the perceived life satisfaction scale. Sch Psychol Int. 1993;14(4):355-360.
26. Losada-Puente L, Araujo AM, Munoz-Cantero JM. A systematic review of the assessment of quality of life in adolescents. Soc Indic Res. 2020;147(3):1039-1057.
27. Fortune N, Badland H, Clifton S, Emerson E, Rachele J, Stancliffe RJ, et al. The disability and wellbeing monitoring framework and indicators: technical report online: Centre of Research Excellence In Disability And Health, Melbourne. 2020. Available from: https://melbourne.figshare.com/articles/online_resource/Disability_and_Wellbeing_Monitoring_Framework_and_Indicators_Technical_Report/12094113.
28. Fortune N, Badland H, Clifton S, Emerson E, Rachele J, Stancliffe RJ, Zhou Q, et al. The Disability and Wellbeing Monitoring Framework: data, data gaps, and policy implications. Aust N Z J Public Health. 2020;44(3):227-232.
    doi pubmed
29. Silva AJ, Caetano A. Validation of the flourishing scale and scale of positive and negative experience in Portugal. Soc Indic Res. 2013;110(2):469-478.
30. Su R, Tay L, Diener E. The development and validation of the Comprehensive Inventory of Thriving (CIT) and the Brief Inventory of Thriving (BIT). Appl Psychol Health Well Being. 2014;6(3):251-279.
    doi pubmed
31. OECD. Economic valuation of environmental health risks to children. OECD. 2006.
32. Braveman P. A new definition of health equity to guide future efforts and measure progress. online: Health Affairs. 2017. Available from: http://vitalysthealth.org/wp-content/uploads/2017/07/Health-Equity-Health-Affairs-Blog-June-2017.pdf.
33. Tsang VWL, Loh L. Using a health equity lens to evaluate short-term experiences in global health (STEGH). Ann Glob Health. 2020;86(1):143.
    doi pubmed
34. World Health Organization. A conceptual framework for action on the social determinants of health online: World Health Organization. 2010. Available from: https://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf.
35. Braveman P, Arkin E, Orleans T, Proctor D, Alonzo P. What is health equity? And what difference does a definition make? online: National Collaborating Centre for Determinants of Health. 2017. Available from: https://nccdh.ca/images/uploads/comments/RWJ_Foundation_-_What_Is_Health_Equity.pdf.
36. National Academies of Sciences Engineering Medicine. Communities in action: Pathways to health equity. online: National Academies of Sciences Engineering Medicine. 2017. Available from: https://www.nap.edu/catalog/24624/communities-in-action-pathways-to-health-equity.
37. Amri MM, Siddiqi A, O’Campo P, Enright T, Di Ruggiero E. Underlying Equity Discourses of the World Health Organization. Social Science Protocols. 2020;3:1-6.
38. Stamatakis KA, Baker EA, McVay A, Keedy H. Development of a measurement tool to assess local public health implementation climate and capacity for equity-oriented practice: Application to obesity prevention in a local public health system. PLoS One. 2020;15(9):e0237380.
    doi pubmed
39. Blanchet Garneau A, Belisle M, Lavoie P, Laurent Sedillot C. Integrating equity and social justice for indigenous peoples in undergraduate health professions education in Canada: a framework from a critical review of literature. Int J Equity Health. 2021;20(1):123.
    doi pubmed
40. Garay JE, Chiriboga DE. A paradigm shift for socioeconomic justice and health: from focusing on inequalities to aiming at sustainable equity. Public Health. 2017;149:149-158.
    doi pubmed
41. Eskandar M, Otadi M, Mojibi T. Ranking the Desirability indicators of Community-Based Rehabilitation (CBR) based on analytical hierarchy process. International Journal of Psychology and Behavioral Sciences. 2018;8(1):7-11.
42. World Health Organization, UNESCO, International Labour Organization, international disability development consortium. Community-based rehabilitation: CBR guidelines. online: World Health Organcization. 2010. Available from: https://www.who.int/publications/i/item/community-based-rehabilitation-cbr-guidelines.
43. Kangmennaang J, Elliott SJ. Towards an integrated framework for understanding the links between inequalities and wellbeing of places in low and middle income countries. Soc Sci Med. 2018;213:45-53.
    doi pubmed
44. United Nations. Convention on the Rights of Persons with Disabilities (CRPD). online: United Nations. 2015. Available from: https://www.un.org/development/desa/disabilities/convention-on-the-rights-of-persons-with-disabilities.html.
45. World Health Organization. Towards a common language for functioning, disability and health ICF. online: World Health Organization. 2002. Available from: https://www.who.int/classifications/icf/training/icfbeginnersguide.pdf.
46. Government of Canada. Social determinants of health and health inequalities. online: Government of Canada. 2020. Available from: https://www.canada.ca/en/public-health/services/health-promotion/population-health/what-determines-health.html.
47. Raphael D, Bryant T, Mikkonen J, Raphael A. Social determinants of health: the Canadian facts. Online. 2020. Available from: https://thecanadianfacts.org/.
48. Hatzenbuehler ML, Pachankis JE. Stigma and minority stress as social determinants of health among lesbian, gay, bisexual, and transgender youth: research evidence and clinical implications. Pediatr Clin North Am. 2016;63(6):985-997.
    doi pubmed
49. World Health Organization. Social determinants of health online: World Health Organization. 2020. Available from: https://www.who.int/social_determinants/en/.
50. Deloria R, Wolbring G. Neuro-advancements and the role of nurses as stated in academic literature and Canadian newspapers. Societies. 2019;9(3):61.
51. Racine E. Neuroscience, neuroethics, and the media. In: Clausen J., Levy N, editors. Handbook of neuroethics. Dordrecht, Netherlands: Springer; 2015. p. 1465-1471.
52. Roskies A. Neuroethics for the new millenium. Neuron. 2002;35(1):21-23.
    doi pubmed
53. Farah MJ. Neuroethics: the practical and the philosophical. Trends Cogn Sci. 2005;9(1):34-40.
    doi pubmed
54. Levy N. Introducing neuroethics. Neuroethics. 2008;1(1):1-8.
55. Soekadar SR, Birbaumer N. Brain-machine interfaces for communication in complete paralysis: Ethical implications and challenges. Handbook of Neuroethics. Oxford, UK: Oxford University Press. 2015. p. 705-724.
56. Wallach W. From robots to techno sapiens: ethics, law and public policy in the development of robotics and neurotechnologies. Law, Innovation and Technology. 2011;3(2):185-207.
57. Fins JJ. Neuroethics, neuroimaging, and disorders of consciousness: promise or peril? Trans Am Clin Climatol Assoc. 2011;122:336-346.
    pubmed
58. Stahnisch FW. History of neuroscience and neuroethics: introduction. Handbook of Neuroethics. Dordrecht, Netherlands: Springer; 2015. p. 461-466.
59. Garden H, Winickoff D. Issues in neurotechnology governance OECD, Paris: ECD Science, Technology and Industry Working Papers, No. 2018/11; 2018. Available from: https://www.oecd-ilibrary.org/content/paper/c3256cc6-en.
60. Racine E. Neuroscience and the media: Ethical challenges and opportunities. In: Illes J, Sahakian BJ, editors. Oxford Handbook of Neuroethics. Oxford, UK Oxford Press; 2012. p. 783-802.
61. Nijboer F, Clausen J, Allison BZ, Haselager P. The Asilomar survey: stakeholders' opinions on ethical issues related to brain-computer interfacing. Neuroethics. 2013;6:541-578.
    doi pubmed
62. Clausen J. Conceptual and ethical issues with brain-hardware interfaces. Curr Opin Psychiatry. 2011;24(6):495-501.
    doi pubmed
63. Hansson SO. Ethical implications of sensory prostheses. Handbook of Neuroethics. 2015:785-798.
64. Bell E, Leger P, Sankar T, Racine E. Deep brain stimulation as clinical innovation: an ethical and organizational framework to sustain deliberations about psychiatric deep brain stimulation. Neurosurgery. 2016;79(1):3-10.
    doi pubmed
65. Cheung J, Wolbring G. Staff’s views from one Canadian organ procurement organization on organ donation and organ transplant technologies: a content analysis. NanoEthics. 2017;11(2):187-202.
66. Einsiedel EF. Commentary: On the position paper of the Ethics Committee of the International Xenotransplantation Association. Transplantation. 2004;78(8):1110-1111.
    doi pubmed
67. Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-inspired artificial intelligence. Neuron. 2017;95(2):245-258.
    doi pubmed
68. Bell AJ. Levels and loops: the future of artificial intelligence and neuroscience. Philos Trans R Soc Lond B Biol Sci. 1999;354(1392):2013-2020.
    doi pubmed
69. Lee J. Brain-computer interfaces and dualism: a problem of brain, mind, and body. AI & Society. 2016;31(1):29-40.
70. Cavazza M, Aranyi G, Charles F. BCI control of heuristic search algorithms. Front Neuroinform. 2017;11:6.
    doi pubmed
71. Buttazzo G. Artificial consciousness: utopia or real possibility? Comp. 2001;34(7):24-30.
72. de Garis H. Artificial brains. Cognitive Technologies. 2007;8:159-174.
73. Catherwood P, Finlay D, McLaughlin J. Intelligent subcutaneous body area networks: anticipating implantable devices. IEEE Technology and Society Magazine. 2016;35(3):73-80.
74. Meeuws M, Pascoal D, Bermejo I, Artaso M, De Ceulaer G, Govaerts PJ. Computer-assisted CI fitting: Is the learning capacity of the intelligent agent FOX beneficial for speech understanding? Cochlear Implants Int. 2017;18(4):198-206.
    doi pubmed
75. Wu YC, Feng JW. Development and application of artificial neural network. Wireless Personal Communications. 2018;102(2):1645-1656.
76. Deloria R, Lillywhite A, Villamil V, Wolbring G. How research literature and media cover the role and image of disabled people in relation to artificial intelligence and neuro-research. Eubios Journal of Asian and International Bioethics (EJAIB). 2019;29(5):169-182.
77. Diep L, Wolbring G. Perceptions of brain-machine interface technology among mothers of disabled children. Disabil Stud Q. 2015;35(4).
78. Sakamaki I, Del Campo CEP, Wiebe SA, Tavakoli M, Adams K. Assistive technology design and preliminary testing of a robot platform based on movement intention using low-cost brain computer interface. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC. 2017.
79. Arpaia P, Duraccio L, Moccaldi N, Rossi S. Wearable brain-computer interface instrumentation for robot-based rehabilitation by augmented reality. IEEE Trans Instrum Meas. 2020;69(9):6362-6371.
80. Alimardani M, Hiraki K. Passive brain-computer interfaces for enhanced human-robot interaction. Front Robot AI. 2020;7:125.
    doi pubmed
81. Simar C, Petieau M, Cebolla A, Leroy A, Bontempi G, Cheron G. EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband. Proceedings of the International Joint Conference on Neural Networks. 2020.
82. Arpaia P, Bravaccio C, Corrado G, Duraccio L, Moccaldi N, Rossi S. Robotic autism rehabilitation by wearable brain-computer interface and augmented reality. IEEE Medical Measurements and Applications, MeMeA 2020 - Conference Proceedings; 2020.
83. Ha J, Kim L. A brain-computer interface-based meal-assist robot control system. 9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021; 2021.
84. Wolbring G, Diep L. Cognitive / neuroenhancement through an ability studies lens. In: Jotterand F, Dubljevic V, editors. Cognitive enhancement. Oxford, UK: Oxford University Press; 2016. p. 57-75.
85. Ball N, Wolbring G. Cognitive Enhancement: Perceptions Among Parents of Children with Disabilities. Neuroethics. 2014;7(3):345-364.
    doi pubmed
86. Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, et al. AI4People-an ethical framework for a good ai society: opportunities, risks, principles, and recommendations. Minds Mach (Dordr). 2018;28(4):689-707.
    doi pubmed
87. Asilomar and AI conference participants. Asilomar AI Principles Principles developed in conjunction with the 2017 Asilomar conference. online: Future of Life Institute. 2017. Available from: https://futureoflife.org/ai-principles/?cn-reloaded=1.
88. IEEE. Ethics in action the IEEE global initiative on ethics of autonomous and intelligent systems, ethically aligned design. online: IEEE. 2018. Available from: https://ethicsinaction.ieee.org/.
89. The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems TIGI. Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems (A/IS). online: IEEE. 2018. Available from: http://standards.ieee.org/develop/indconn/ec/ead_v2.pdf.
90. Participants in the Forum on the Socially Responsible Development of AI. Montreal declaration for a responsible development of artificial intelligence. online: Forum on the Socially Responsible Development of AI. 2017. Available from: https://www.montrealdeclaration-responsibleai.com/the-declaration.
91. Partnership on AI. Tenets. online: Partnership on AI. 2018. Available from: https://www.partnershiponai.org/tenets/.
92. European Group on Ethics in Science and New Technologies. Statement on artificial intelligence, robotics and ‘Autonomous’ systems. online: European Commission. 2018 Available from: https://ec.europa.eu/research/ege/pdf/ege_ai_statement_2018.pdf.
93. Yuste R, Goering S, Arcas BAY, Bi G, Carmena JM, Carter A, Fins JJ, et al. Four ethical priorities for neurotechnologies and AI. Nature. 2017;551(7679):159-163.
    doi pubmed
94. Aicardi C, Fothergill BT, Rainey S, Stahl BC, Harris E. Accompanying technology development in the human brain project: from foresight to ethics management. Futures. 2018;102(September):114-124.
95. Aicardi C, Reinsborough M, Rose N. The integrated ethics and society programme of the Human Brain Project: reflecting on an ongoing experience. Journal of Responsible Innovation. 2018;5(1):13-37.
96. Ienca M. Democratizing cognitive technology: a proactive approach. Ethics Info Tech. 2019;21:267-280.
    doi
97. Burwell S, Sample M, Racine E. Ethical aspects of brain computer interfaces: a scoping review. BMC Med Ethics. 2017;18(1):60.
    doi pubmed
98. Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Libr J. 2009;26(2):91-108.
    doi pubmed
99. Davis K, Drey N, Gould D. What are scoping studies? A review of the nursing literature. Int J Nurs Stud. 2009;46(10):1386-1400.
    doi pubmed
100. Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 2005;15(9):1277-1288.
     doi pubmed
101. Edling S, Mooney Simmie G. Democracy and emancipation in teacher education: A summative content analysis of teacher educators' democratic assignment expressed in policies for Teacher Education in Sweden and Ireland between 2000-2010. Citizenship, Social and Economics Education. 2017;17(1):20-34.
102. Friedman C. Social determinants of health, emergency department utilization, and people with intellectual and developmental disabilities. Disabil Health J. 2021;14(1):100964.
     doi pubmed
103. Nixon SA. The coin model of privilege and critical allyship: implications for health. BMC Public Health. 2019;19(1):1637.
     doi pubmed
104. Williams AE. Good, Better, Best: the human quest for enhancement summary report of an invitational workshop convened by the scientific freedom, responsibility and law program American Association for the Advancement of Science 1-2 June 2006. online: American Association for the Advancement of Science (AAAS). 2006. Available from: http://www.aaas.org/sites/default/files/migrate/uploads/HESummaryReport1.pdf.
105. Wolbring G, Ghai A. Interrogating the impact of scientific and technological development on disabled children in India and beyond. Disability and the Global South. 2015;2(2):667-685.
106. Wolbring G, Ball N. Nanoscale science and technology and people with disabilities in asia: an ability expectation analysis. Nanoethics. 2012;6(2):127-135.
     doi pubmed
107. Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, Samri MA, et al. Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Global Health. 2020;16(1):52.
     doi pubmed
108. Baral S. Jugaad Culture Amidst COVID-19: a time to step up for innovation in low-income countries. Journal of Lumbini Medical College. 2020;8(1):3.
109. Asthana S, Gibson A. Health care equity, health equity and resource allocation: towards a normative approach to achieving the core principles of the NHS. online: Radical Statistics. 2008. Available from: https://www.radstats.org.uk/no096/AsthanaGibson96.pdf.
110. Mannan H, MacLachlan M, McAuliffe E. The human resources challenge to community based rehabilitation: the need for a scientific, systematic and coordinated global response. Disability, CBR & Inclusive Development. 2012;23(4):6-16.
111. Djebrouni M, Wolbring G. Impact of robotics and human enhancement on occupation: what does it mean for rehabilitation? Disabil Rehabil. 2020;42(11):1518-1528.
     doi pubmed
112. Zardiashvili L, Fosch-Villaronga E. “Oh, Dignity too?” Said the Robot: human dignity as the basis for the governance of robotics. Mind Mach. 2020;30:121-143.
     doi
113. Various. Interventions in disabled childhood studies. Sheffield: iiHuman Press 2020; 2020. Available from: https://biblio.ugent.be/publication/8672645/file/8672646.
114. Wolbring G. Hearing beyond the normal enabled by therapeutic devices: the role of the recipient and the hearing profession. Neuroethics. 2013;6:607-616.
     doi pubmed
115. Lee JH, Sadana S. Commission on social determinants of health. Improving equity in health by addressing social determinants. online: Wolrd Health Organization. 2011. Available from: http://apps.who.int/iris/bitstream/10665/44793/1/9789241503037_eng.pdf.
116. Sharma M, Pinto AD, Kumagai AK. Teaching the social determinants of health: a path to equity or a road to nowhere? Acad Med. 2018;93(1):25-30.
     doi pubmed
117. Muhajarine N, Labonte R, Winquist BD. The Canadian index of wellbeing: key findings from the healthy populations domain. Can J Public Health. 2012;103(5):e342-347.
     pubmed
118. Jensen E. The UNESCO Recommendation on science and scientific researchers will transform working conditions, rights and responsibilities of researchers globally. online: LSE Impact Blog. 2020. Available from: https://blogs.lse.ac.uk/impactofsocialsciences/2020/10/05/the-unesco-recommendation-on-science-and-scientific-researchers-will-transform-working-conditions-rights-and-responsibilities-of-researchers-globally/.
119. National Academies of Sciences, Engineering, and Medicine. The endless frontier: the next 75 years in Science. Washington, DC: The National Academies Press. 2020.
120. Dankwa-Mullan I, Lee Scheufele E, Matheny M, Quintana Y, Chapman W, Jackson G, et al. A proposed framework on integrating health equity and racial justice into the artificial intelligence development lifecycle. J Health Care Poor Underserved. 2021;32:300-317.
121. Buchman DZ, Wadhawan S. A global vision for neuroethics needs more social justice: brain imaging, chronic pain, and population health inequalities. AJOB Neurosci. 2019;10(3):130-132.
     doi pubmed
122. Emens EF. Disability admin: the invisible costs of being disabled. online: Minnesota Law Review. 2021. Available from: https://minnesotalawreview.org/article/disability-admin-the-invisible-costs-of-being-disabled/.
123. Emens EF. Life admin: how I learned to do less, do better, and live more. Boston: Houghton Mifflin. 2019.

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial 4.0 International License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Journal of Neurology Research is published by Elmer Press Inc.