Ethical Space for a Sensitive Research Topic: Engaging First Nations Women in the Development of Culturally Safe Human Papillomavirus Screening
Bibliographic record
Abstract
Human papillomavirus (HPV) is a sexually transmitted infection (STI) and the main risk factor for cervical cancer. Cervical cancer is highly preventable with regular screening, especially when using HPV testing. In Canada, an up to 20-fold higher rate of this cancer has been reported in First Nations women compared to the mainstream population, possibly associated with under-screening, barriers to follow-up treatment, and a pervasive lack of access to culturally safe screening services. As a foundation for the development of culturally safe screening methods in First Nations communities in northwest Ontario, we have developed a participatory action research approach based on respectful and meaningful collaboration with First Nations women, community health care providers, and community leaders. Being mindful of the schism that exists between Western public health approaches to cervical cancer screening and First Nations women’s experiences thereof, we adopted Ermine’s interpretation of ethical space to initiate dialogues with First Nations communities on this sensitive topic. We used an iterative approach to continuously widen the ethical space of engagement through several cycles of increasing dialogue with First Nations stakeholders. This approach resulted in a rich exchange of knowledge between community stakeholders and our research team, leading to the development of a shared plan for First Nations HPV research. Because of this successful engagement process, a pilot study in one First Nations community in northwest Ontario has been completed and there is support from ten First Nations communities for a large-scale study involving up to 1,000 women. Ethical space served as the foundation for a meaningful dialogue in this participatory action research approach and can be adapted to fit other research projects in similar settings.
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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 | 0.188 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.037 | 0.065 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.013 | 0.021 |
| 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".