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Record W2729570848 · doi:10.1093/jlb/lsx019

Biobank donors and the concept of benefit: time for reciprocity

2017· article· en· W2729570848 on OpenAlexaff
Ma’n H. Zawati, Michael Lang

Bibliographic record

VenueJournal of Law and the Biosciences · 2017
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsMcGill UniversityMcGill Genome Centre
Fundersnot available
KeywordsBiobankIntellectual propertyReciprocity (cultural anthropology)Engineering ethicsPolitical scienceProperty (philosophy)Responsible Research and InnovationValue (mathematics)Law and economicsBusinessPublic relationsSociologyLawComputer scienceSocial scienceEngineeringEpistemologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

Helen Yu's insightful article entitled ‘Redefining responsible research and innovation for the advancement of biobanking and biomedical research’ highlights the potentially crucial role of intellectual property regimes in encouraging and facilitating genomics innovations. Yu claims that one of the central objectives in ‘responsible research and innovation’ is the maximization of the value of publically funded research.1 She contends that this entails the return of benefit to society through, generally speaking, the production of new innovations. But this understanding, according to Yu, gives rise to novel challenges to the logic of intellectual property. This is so because patent law requires that new technologies not have undergone previous disclosure, while principles of open science require real time disclosure.2 This tension is heightened because the protections of intellectual property law are necessary for the safeguard of research and development. As a consequence of this, responsible research and innovation plays a delicate balancing act involving a range of stakeholders. Yu proposes a ‘holistic innovation framework’3 in which social engagement is targeted toward ensuring that members of the stakeholder community understand that the process of innovation is a complex multistakeholder endeavor in which participants play ‘an essential role in contributing to the successful development of basic research into socially beneficial outcomes’.4 According to her, donors, the industry, the government, researchers, universities, and the public are all stakeholders vested with competing interests. She contends that mechanisms should be put in place to allow for the recognition of relevant stakeholder motivations and enable ‘stakeholders to receive the specific benefit they expect in exchange for their contribution to the innovation process’.5 More specifically, for Yu, participation is most efficiently incentivized by the extraction of some reward or ‘quid pro quo’ that is indispensable in the research process.6 While one can agree with the importance of engaging stakeholders early on in the process, Yu's article could have benefited from a more nuanced discussion around the concept of benefit when it comes to donors. Indeed, to constrain the motivations of donors to the protection of their privacy and the need to benefit from the research that uses their samples does not take into account the practical realities on the ground or the complex motivations of biobank donors. For Yu, the motivations of donors seem to turn largely on a concern for individual benefit, while larger social benefit is considered relevant only for public support of genomics research.7 In consequence, Yu claims that donors tend to be less comfortable with contributing to research in which industrial or government actors, rather than universities, play important roles. According to her, this can be explained by appeal to her proposed framework of donor motivation. But this framework, as we will see below, may be incomplete. First, the definition of biobanks provided by Yu is generic and does not consider the increasingly rich tapestry of biobanks.8 Being cognizant of the varying typology of biobanks in one's assessment of legal, ethical or policy matters is essential. Indeed, biobanks created by public health authorities are different from residual samples biobanks; and disease-specific biobanks are different from population biobanks. Each type involves different goals, varying methods of recruitment, researcher–participant relationships, levels of access, and much more.9 While individual benefit might be a relevant concept in disease-specific biobanks, the same cannot be said of population biobanks, which the article by Yu should have put more emphasis on given her interest for the translation of research into public benefit. One interesting characteristic of population biobank donors is that they are specifically told that they are not expected to directly benefit from participation, since the ultimate goal of these projects is to improve the health of future generations. This illustrates the many difficulties of adhering to an individualistic conception of benefit. Indeed, authors have increasingly interpreted benefit as profiting ‘whole population groups […]’10 away from individualistic calculations. Some authors have based this shift on the concept of justice11 and others have been motivated by the principle of fairness. These shifts, in turn, are based on considerations of justice and solidarity.12 Similar to the HUGO and UNESCO normative documents, some authors have described the shift through the ‘reasonable availability’13 lens. They assert that reasonable availability ‘requires that research be tailored to the health needs of the host community and that research results thus be made available to the community at the end of the project’.14 This last understanding of benefit sharing dominates current population biobanking practice as is evidenced in Table 1 illustrating passages from consent forms and information brochures directly provided to participants by some Canadian population biobanks. Considering this, it will be difficult to design an engagement process as proposed by Yu that encompasses an ‘incentive’ unlikely to materialize in practice. Does this mean that participants should not expect any form of exchange? The answer is no. In a systematic review of the literature, authors have examined why participants enroll in population biobank studies and found that ‘[a]mong the personal attitudes, values such as altruism and trust were corroborated as central elements influencing participation’,15 something that Yu mentions in her article. But while the literature has often described biobank participants as altruists,16 such descriptions tend to be superficial at best, where the authors provide no in-depth analysis or attend to relevant nuances. Pure selflessness cannot reasonably be maintained as the primary reason for participation. This reality is not new: the apparent paradox between the acknowledged value of altruism and the prevalence of the expectation of personal benefit needs to be further considered. Our results confirmed previous work that had shown that the motivation to participate in population genetic databases is a ‘combination of self-interest and altruism’ and that both these motives can vary in intensities.17 There's a difference between expectation, the existence of an exchange and the nature of that exchange. It is clear that some donors will be interested in receiving some sort of benefit, but given that population biobanks are unlikely to deliver, what will the nature of the ‘quid pro quo’ be in such case? We contend that such exchange can only materialize through reciprocity; a concept based ‘on the fundamental recognition of human beings as social creatures, able to give and to receive benefits’.18 In our particular context, reciprocity ‘provides a way of protecting the altruist by emphasizing the value of exchange’.19 According to some authors, there exist two forms of reciprocity: one premised on conditions of reasonable exchange and the other that sees such exchange as incidental to the act.20 In the first form, which could be seen as similar to what Yu is suggesting, the act of benefiting the other (future generations) is conditioned on some form of return. Such condition, that is, the exchange of some set of benefits, is crucial to the fulfilment of the act in question. In order to satisfy the conditions of this form of reciprocity, the consent documentation would have needed to include clear and unequivocal mention of benefit to the participant. Given the nature of these projects, this would hardly be possible. No population biobank can inform prospective participants that their participation will lead to the receipt of direct personal benefit. Such a condition is inimical to this kind of observational and longitudinal research where general benefits aimed at future generations are likely to arise only many years ahead. Therefore, the first form of reciprocity in the interpretation of altruism does not fit into the current consent practices of population biobanks. This brings us to the second form, which sees a potential exchange as incidental to the act, rather than it being a condition of it. Many similarities can be made with population biobank projects. In their case, participants enroll in these projects for the better of future generations while understanding that they will likely not benefit from participation. Some may wish or be motivated by self-interest—as previous studies have shown21—but they will be told that it is not likely to happen. Their enrolment signals their willingness to participate without such precondition that they benefit from it. If any direct benefit arises in the future, it can only be incidental to their generous act. Would this still be considered a form of altruism? According to Niall Scott and Jonathan Seglow—authors of the seminal book entitled ‘Altruism’—the answer is yes: reciprocation, if it is to remain truly altruistic, needs to avoid the encroaching egoism that can muddy the motivational waters were equal treatment is expected, or a recipient is held to an obligation rather than being free to make an independent decision about giving back in return.22 True benefit in biobanking can be realized through the maximization of collaboration between biobanks and researchers. Indeed, the data and biological samples collected through population biobanks will constitute a reference map for local and international researchers, allowing them ‘to validate, compare, or replicate their studies or use the data and samples as controls’.23 While such cooperation could arguably be considered a scientific imperative,24 it is also an ethical one based on the promise made to participants to utilize and maximize their donation to further scientific knowledge. The sharing of data contributes to the aim of producing generalizable results and—in the particular case of biobanks—benefiting society through a better understanding of common diseases and their etiology.25 This will likely cloud the argument that the dichotomy of industry and government-led projects can still be relevant as presented by Yu. In fact, to the extent ‘reciprocal altruism’ as described above is true, we might have good reason to think that Yu overstresses the mistrust of corporate and government research, which might not be endemic. In sum, donor motivations, after all, are more complex than simple concern for derived personal benefit. Rather, while donors, to some degree, may expect to benefit personally from biobank participation, this is not always possible as it is in the case of population biobanks. Rather, donors may be motivated by the hope that their samples will be used in such a way as to draw out every ounce of available scientific understanding from the research in question. Donors, on this modified view, may be more willing to accept such participation in furtherance of scientific objectives of biobanks, regardless of whether it is led by industry or the government. Consent provisions addressing benefits from Canadian population biobanks. Consent provisions addressing benefits from Canadian population biobanks. The authors acknowledge the financial support of the Canadian Partnership Against Cancer and Le Fonds de partenariat pour un Québec innovant et en santé (FPQIS) through the Quebec – Clinical Research Organization in Cancer (Q-CROC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.046
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.065
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.050
Scholarly communication0.0150.027
Open science0.0020.012
Research integrity0.0170.015
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.314
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations7
Published2017
Admission routes1
Has abstractyes

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