How do institutions assess progress on equity, diversity, inclusion, and anti-oppression? A scoping review protocol
Bibliographic record
Abstract
Healthcare professions and institutions have been implored to re-examine how established practices across patient care, administration, and professional training contribute to health inequities among diverse patient populations (Canadian Public Health Association, 2018; Dryden & Nnorom, 2021; Olayiwola et al., 2020). Several institutions have publicly acknowledged racism and other systems of oppression are systemically embedded within the healthcare system, while committing to prioritizing equity, diversity, and inclusion (EDI) as well as instituting anti-oppression efforts (Canadian Public Health Association, 2018; College of Family Physicians of Canada, 2021; College of Physicians and Surgeons of Ontario, 2022; Government of British Columbia, 2020). Similarly, academic institutions (Dewidar et al., 2022; Mori, 2022) and businesses (Baum, 2021; Bohonos & Sisco, 2021) have been called to meaningfully address EDI issues within their own organizations. Recent literature in this landscape is replete with calls to action and recommended initiatives for different institutions. For example, a recent scoping review (Hassen et al. 2021) that examined anti-racism interventions in healthcare settings, recommended strategies such as (1) use a multi-level, long-term approach; (2) embed racial equity policies and procedures (e.g., hiring, retention, promotion); (3) relate mandatory training to broader systems of power, hierarchy, and dominance; and (4) integrate mechanisms for self-reflections. Whereas such recommendations are useful in strategizing future efforts, there is scant literature on how to assess progress or performance of EDI and anti-oppression (EDIAO) initiatives, including poor understanding about how to measure or monitor change mechanisms or identify performance indicators. Notwithstanding, to ensure progress and demonstrate public accountability, institutions (including healthcare, academia, and businesses) must cultivate better understanding of how to assess organizational functioning regarding EDIAO. Correspondingly, our research explores the various ways in which institutional progress on EDIAO may be monitored or assessed in healthcare and other institutions, including academia and businesses. Further, EDIAO research with healthcare institutions remain limited with regard to strategies to evaluate impact of EDIAO initiatives; thus, examining other types of institutions, such as academia and businesses, might provide some insights. We use a scoping review methodology (Arksey & O’Malley, 2005; Tricco et al., 2018) to examine pertinent literature for the variable approaches to tracking progress and outcomes for institutional-level EDIAO initiatives across healthcare, academia, and businesses. This will include and overview of the types of tools and frameworks that may be employed to facilitate outcome identification, monitoring, and measurement.
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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 | 0.235 | 0.216 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".