HPV and Cervical Cancer Prevention in Refugee Women: An Intersectional Approach
Bibliographic record
Abstract
Background: HPV-related cervical cancer (CC) is Canada’s 4th most commonly diagnosed cancer in women aged 14-44. Refugee women resettling in the Greater Toronto Area (GTA) due to conflict, human rights violations, and climate disasters in their homelands are at an increased risk for HPV-related cancer. Refugee women are at an elevated risk for HPV-related CC due to barriers to accessing routine sexual health and HPV-preventative care. Cultural differences, lack of awareness of sexual and reproductive health, and trauma exacerbate the risk of HPV in women arriving from developing nations. Existing services in the GTA fail to reach this population due to growing gaps in intersectional care that consider women's various backgrounds. The knowledge gaps in healthcare services about intersectional refugee women perpetuate an increased burden on this population.
 Objective: This scoping review aimed to understand the different barriers that refugee women who resettle in the GTA experience. Furthermore, the review was also done to understand how an intersectional approach facilitating health promotion about sexual and reproductive healthcare can increase routine uptake of HPV screening preventative services in the GTA.
 Methods: A search on PubMed, JSTOR, and MEDLINE was done using terms for HPV, CC, refugee women, screening, Pap tests, and HPV vaccines. Articles analyzing equitable and intersectional care to address knowledge gaps were included. Results: 29 articles were included in this review. The literature review results highlighted that refugee women in the GTA experience a disproportionate risk rate for HPV-related CC. This disease burden was correlated with the inadequacy of programs explicitly targeted at refugee women who require an intersectional approach to address the knowledge gaps.Conclusion: Culturally appropriate and patient-centred care is needed to reduce HPV-related CC rates among refugee populations in the GTA. This includes knowledge translation and accessible health literacy programs to bridge the gap between refugees and healthcare providers. The lack of intersectional care is a common barrier for refugee women in accessing preventative services to reduce HPV. Thus, it is essential to focus on public health initiatives that can increase awareness and provide trauma-informed and culturally appropriate care to improve outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".