A quality improvement initiative to strengthen equity, diversity and inclusion and anti-racism considerations in the IDEA Framework
Bibliographic record
Abstract
Trillium Health Partners' (THP's) Regional Ethics Program led a quality improvement project to explicitly address equity, diversity, and inclusion and anti-racism and anti-oppression in its IDEA: Ethical Decision-Making Framework. Various groups, encompassing diverse backgrounds and lived experiences, completed a short survey including demographic and open-ended questions. Survey responses revealed gaps within the IDEA Framework and recommendations for modifications (e.g., editing language to be more accessible and inclusive, placing a greater focus on lived experience). Several themes emerged including explicitness, simplification, and continued learning. This work is of particular interest to health leaders as it aims to expose where bias, power, and privilege exist when addressing ethical dilemmas within healthcare systems. It explicitly addresses implicit bias, discrimination and harassment, reflexivity in care, as well as re-defines and re-imagines ethical principles (e.g., accountability, diversity, inclusivity, justice, relationships, and trust).
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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 | 0.222 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".