What comes after strategy: Moving beyond statements and open letters – An analysis of three Toronto hospitals’ diversity, equity and inclusion (DEI) plans
Bibliographic record
Abstract
Introduction The murders of Breonna Taylor and George Floyd in 2020 forced institutions to publicly acknowledge systemic racism. In the Canadian healthcare sector, some hospitals used this pivotal moment to create strategic equity plans to address anti-Black racism and ongoing health inequities. Methods Through a case study approach, we selected three hospitals in Toronto, Canada and analysed their most recent publicly available diversity, equity and inclusion (DEI) strategic plans. Results All three hospitals released new DEI strategies following 2020 that covered similar grounds: incorporating DEI into HR practices, cultural adaptations of services, race-based data collection and investments in training. While two out of three hospitals reported progress on their anti-Black racism commitments, specific actions to be taken and metrics to monitor and track progress varied. Conclusions DEI plans analysed are set to reach maturity as early as 2023 and as late as 2025. We provide high level recommendations to guide this work beyond these timelines. Antiracism reform and reconciliation is not a one-time event, but requires thoughtful planning, collaboration with communities, investment in labour (ie, resources and staff), reflection and deep reckoning.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".