Having Hard Conversations About Racism Within Nursing Education: A Collaborative Process of Developing an Antiracism Action Plan
Bibliographic record
Abstract
Background: Multiple events that occurred in the United States in early 2020 prompted a widespread response to address racism that exists within systemic and social structures. Third-year psychiatric nursing students at a small Western Canadian university answered the call to action by initiating a process to address racism within clinical and educational settings in their faculty. Methods: The researchers used collaborative autoethnography to examine the experience of students and faculty working collaboratively to create a Faculty of Health Studies antiracism action plan. Results: The reflections of the student and faculty researchers highlighted three major themes: what inspired the work of creating an antiracism action plan, doing the work, and lessons learned. Conclusion: Engaging in this research provided an opportunity to critically reflect on the process of students and faculty working together in establishing an antiracism action plan. [ J Nurs Educ . 2022;61(8):461–468.]
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 | 0.109 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.033 | 0.032 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".