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
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
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.
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 | 0.022 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.078 |
| Research integrity | 0.000 | 0.001 |
| 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