Understanding the relationship among discrimination, resource availability, health and workplace outcomes in ethnic minority nursing staff in Canada
Bibliographic record
Abstract
PURPOSE: Discrimination against ethnic minority nursing staff is a serious concern in healthcare and the COVID-19 pandemic has brought it to the forefront. The purpose of this survey study was to investigate the predictive relationship among discrimination experiences, resource availability, health and work outcomes among ethnic minority nursing staff during the COVID-19 pandemic. DESIGN/METHODOLOGY/APPROACH: A survey was conducted among ethnic minority nursing staff in Canada during the COVID-19 pandemic. Respondents were asked to report their experiences of discrimination, perceived social support, resilience, health-related quality of life (HRQoL) and teamwork value using previously validated instruments. Sequential regression analysis was conducted to address the study aim. FINDINGS: The respondents reported that discrimination occurred both in the workplace and on public transit and could take various forms, from verbal harassment to physical assault. Discrimination experience and resource availability, including resilience and social support, were predictive of HRQoL. PRACTICAL IMPLICATIONS: Managers and administrators are urged to promote diversity and inclusion in the workplace and provide resources to support resilience and social support of ethnic minority nursing staff. ORIGINALITY/VALUE: The findings suggested that discrimination and racism manifest subtly in various forms and occur everywhere. The study contributes to the limited level of understanding of the vulnerable populations framework in the context of ethnic minority nursing staff and workplace outcomes. It also provides evidence about the impacts of discrimination on ethnic minority nursing staff during the second year of the COVID-19 pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".