Equity, diversity and inclusion: exploring Canadian nurses’ perceptions
Bibliographic record
Abstract
BACKGROUND: Canadian nurses have a long history of promoting social justice through their work, yet patients continue to experience discrimination in Canadian healthcare services. Advancing nurses' commitment to work towards equitable, diverse and inclusive nursing care requires an understanding of their perceptions and experiences in relation to equity, diversity and inclusion (EDI). AIM: The overall aim of the study was to enhance understanding of the perceptions and experiences of nurses in Alberta, Canada, in relation to EDI. METHOD: This article reports the quantitative results. A cross-sectional quantitative design was used. An anonymous online survey was developed incorporating a sociodemographic questionnaire and the Perceived Discrimination Scale, the Workplace Prejudice/Discrimination Inventory Scale and the Perceived Ethnic Discrimination Questionnaire - Community Version. A total of 104 nurses completed the survey. RESULTS: Respondents who belonged to any racialised group were more likely to perceive discrimination than those who did not belong to any racialised group. Age, gender and number of years of nursing practice had no relationship with perceived discrimination, indicating that nurses from racialised groups may continue to experience discrimination throughout their careers. CONCLUSION: The study provides evidence that some groups of nurses in Canada experience racism and discrimination in the workplace. This has implications for their mental health and well-being and for their career progression. Nurse leaders should consider how to ensure equitable access to career development opportunities for nurses who are underrepresented in leadership. In addition, processes should be implemented in healthcare organisations that provide support to nurses from racialised groups throughout their careers.
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".