Effective diversity, equity, and inclusion practices
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
Demographics in Canada, and the workplace, are changing. These include population changes due to race, ethnicity, religion/faith, immigration status, gender, sexual identity and orientation, disability, income, educational background, socioeconomic status, and literacy. While this rich diversity can present challenges for patient experiences/outcomes and working environments, it can also present opportunities for positive transformation. For successful transformation to take place, strategies should focus on "Diversity, Equity, and Inclusion" (DEI) versus "diversity" alone and on creating inclusive team environments for positive staff experiences/engagement. There is a growing understanding of the relationship between the providers' work environments, patient outcomes, and organizational performance. This article leverages the principle of improving the healthcare provider's experience based on Health Quality Ontario's Quadruple Aim ("people caring for people"). Based on learnings/experiences, the top three successful practices from the organization's DEI strategy have been outlined in this article.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.052 |
| 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