Incorporating Equity, Diversity and Inclusion in a Canadian Biobank
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) is an established concept and is an important issue in health research. It is now recognized that measures to address EDI in research can have a positive impact on the value of health research outputs and health outcomes based on this knowledge. EDI strategies, guidelines, and education and training are now embraced by national research funders and local research organizations. However, these initiatives are very broad and not specific to the field of biobanking. We have, therefore, set out to develop and implement a formal research biobank EDI action plan. This article describes the creation of an EDI action plan that provides an intentional approach to identifying and achieving EDI actions and priorities for our research biobank. The plan is framed by the definitions of EDI and an understanding of the topics, issues, and groups within the EDI field. The plan is founded on a set of guiding principles and delineates three pillars of work that align with team, participant, and researcher domains. The plan identifies a set of 31 actions that are categorized by implementation time frames, in order to positively address EDI issues across these pillars. The completion of these actions will help us to mitigate against bias and enrich our biobanking and research services. Ultimately, our goal is to realize more diverse participation in research supported by our biobank. This would support health research to explore and better understand differences in disease biology and the efficacy of medical treatments across all people.
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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 | 0.141 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.039 | 0.016 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".