What comes after strategy: Moving beyond statements and open letters – An analysis of three Toronto hospitals’ diversity, equity and inclusion (DEI) plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.031 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it