Hearing Our Voices: A Descriptive Process of Using Film for Anti-racist Action in Nursing
Bibliographic record
Abstract
Racism in healthcare is real and it impacts nurses in ways that permeate the culture of healthcare. In the context of increasing social discourse about racism in healthcare, a group of nurses in British Columbia, Canada, felt a moral obligation to expose the social injustice of the systemic racism they had witnessed or experienced. They used film, an arts-based medium, as an innovative tool with the potential to reach an array of viewers, for this nurse activist project in anti-racist action. The creative process allowed for a racially diverse group of nurses to engage in meaningful dialogue about racism in healthcare. The purpose of this descriptive methodological article is to describe how a creative team of novice nurse filmmakers used the nursing process as a framework to carry this project from concept to execution. The stages described include the rationale for developing the film, the process of utilizing this as a means of nurse activism, and the value of using film as a strategy for social activism. Film was used to engage nurses and nursing students in anti-racist work that critically challenges the structural racism embedded in healthcare. We request that all readers view our film in conjunction with reading this article to best grasp how this article and the film complement one another because the film and article are intended to co-exist and not to exist in isolation from one another.
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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".