Community Engagement and the Ethical Governance of Canadian COVID-19 Biobanks
Bibliographic record
Abstract
Canadian COVID-19 biobanks are valuable resources for public health research on the SARS-CoV-2 virus and will continue to be so in the coming years. As is true for all biobanks, it is necessary to ensure that these biobanks are well-governed to ensure their sustainability and ethical conduct. However, Canadian COVID-19 biobanks were established under the pressure of distinct challenges, such as time and physical barriers. The impacts of these barriers were exacerbated by a lack of pre-existing guidance on developing biobank governance policies within the context of a public health emergency. The literature has since offered guidance to help define ethical biobank governance during a public health emergency; however, there is a notable gap on the role of community engagement for such COVID-19 biobanks. Beyond the pandemic context, biobanks are increasingly turning to community engagement committees or other community engagement strategies to improve the trust, relevance, and transparency of biobank research. In this thesis, I examine the role of community engagement in biobanking in the context of Canadian COVID-19 biobanks. To consider how this is being managed in the Canadian COVID-19 context, I analyzed the available internal governance policies of Canadian COVID-19 biobanks to determine the extent of their community engagement efforts. I informed this document analysis with an assessment of Canadian and international guidance on biobank governance, as well as a literature review on the topic of good governance of COVID-19 biobanks
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".