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Record W127223771

Research Ethics in 2020: Strengths, Weaknesses, Opportunities, and Threats

2011· article· en· W127223771 on OpenAlexvenueaboutno aff
Michael McDonald, Daryl Pullman, James A. Anderson, Nina Preto, Heather Sampson

Bibliographic record

VenueHealth law review · 2011
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsResearch ethicsCorporate governancePolitical scienceContext (archaeology)BiobankStrengths and weaknessesBioethicsAccreditationPublic relationsEngineering ethicsLawPsychologyBusinessEngineering
DOInot available

Abstract

fetched live from OpenAlex

In this special issue of Health Law Review, it seemed especially appropriate to offer an ethics assessment of current (2010) and future state (2020) of governance for health research in Canada. It is ten years since McDonald report for Law Commission of Canada (LCC) on The Governance of Health Involving Human Subjects. (2) In last decade there have been significant developments in health research [e.g., genomics, biobanks, stem cell research, major cohort studies] and in mechanisms used to ensure adequate protection for research participants [e.g., accreditation in US, a second edition of Tri-Council Policy Statement on Ethical Conduct of Involving Humans (TCPS)], as well as significant problems reported at domestic and foreign research institutions. In addition to LCC report, there have been numerous commentaries on these developments including two special issues of HLR on research ethics governance published by our network and its precursor. (3) This paper proceeds in four parts. In introductory section, we explain what we mean by the Canadian governance for health research. We also offer a brief description of SWOT analysis, and offer a broad characterization of Canadian health research context as background for this analysis. In Part 2, we list what we see as strengths and weaknesses of our current of protection. In Part 3, we project ten years into future to consider potential opportunities and threats that are likely to beset our governance system. In Part 4, finally, we discuss our results, indicate limitations of our analysis, and draw some general conclusions. Part 1. Introduction Part 1 (a) The Canadian Governance System for Health It may seem strange to talk about a Canadian governance for (human) health research because system is a term that seems ill suited to complex set of standards, policies, procedures and practices that constitute participant protection in Canadian health research.4 There are macro-level actors who set general parameters (including governments, professional bodies and research sponsors), meso-level institutions (research institutions, sites where research is conducted) and individuals (including researchers and research workers). Moreover, there is an array of actual and potential authorities for Canadian research protection - international, federal provincial, institutional and professional - as well as a complex mix of domestic and international interests and interest groups - commercial, public, consumer, political, etc. that often act at cross-purposes. Despite these complexities, we believe there are insights to be gained from taking a systems view of our current patchwork of regulation and oversight. For purposes of this analysis, we take as our starting point Health Involving Human Subjects (HRIHS) lifecycle advanced in Research Ethics Broadly Writ: Beyond REB Review (5) by Anderson et al., which also appears in this issue of Health Law Review. In Anderson article, HRIHS lifecycle is described in terms of twelve linked and sequential stages that, together, offer a (admittedly idealized) description of various processes involved in conducting HRIHS: [ILLUSTRATION OMITTED] In our view, Canadian governance has a role to play at each stage in HRIHS lifecycle. However, as Anderson and co-authors note, there is a major gap between what governance is supposed to do, and what it actually does. Elucidating risks and opportunities posed by this gap is subject of this paper. Part 1 (b) SWOT Analysis SWOT and SWOT-type analyses are widely used in world of business and government. The basic idea is to assess internal capacity of an organization or [its strengths and weaknesses! to deal with external environments [opportunities and threats]. …

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)0.284
metaresearch head score (Gemma)0.363
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2840.363
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0150.037
Scholarly communication0.0320.024
Open science0.0060.012
Research integrity0.0220.034
Insufficient payload (model declined to judge)0.0040.001

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 head0.918
GPT teacher head0.700
Teacher spread0.218 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreReview

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".

Quick stats

Citations5
Published2011
Admission routes2
Has abstractyes

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