Examining healthcare staff views and experiences with equity, diversity, and inclusion (EDI) in a multi-disciplinary healthcare setting: A mixed methods needs assessment to advance inclusive excellence
Bibliographic record
Abstract
Equity, diversity, and inclusion (EDI) is increasingly identified as a priority in healthcare organizations and an essential component of high-quality care. However, research on advancing EDI in healthcare workplaces is limited. This study sought to elucidate how to advance inclusive excellence in a clinical department of a comprehensive cancer centre. A mixed-methods quality improvement project was undertaken whereby staff completed an online survey, and a sub-group were interviewed. Quantitative data were summarized using descriptive statistics and univariate regression analyses and qualitative data were analyzed using thematic analysis. 103 of 219 staff/learners completed the survey and 17 staff were interviewed. Over 90% of survey participants agreed EDI should be a priority and 29% had experienced discrimination, which was associated with considering leaving the organization. Facilitators to EDI were: enthusiasm/awareness of EDI, openness to new ideas, gender diversity, and safe environments for self-expression. Barriers to EDI were lack of: EDI knowledge, cohesion/collaboration, psychological safety, diversity along various dimensions, EDI-related communication, and burnout. To advance departmental EDI, initiatives should leverage facilitators and overcome barriers to meet department needs aligning with organizational goals. These findings will inform the development of a story huddle learning series to strengthen EDI-related knowledge and skills.
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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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".