Ethnographic study of the barriers and facilitators to implementing human papillomavirus (HPV) self-sampling as a primary screening strategy for cervical cancer among Inuit women of Nunavik, Northern Quebec
Bibliographic record
Abstract
The rate of cervical cancer among Canadian Inuit women is higher than the national average. To date, early detection remains the best strategy for reducing the incidence of cervical cancer and its consequences. Thus, the aim of this study was to explore the barriers and facilitators in implementing human papillomavirus (HPV) self-sampling as a primary screening strategy for cervical cancer among Inuit women of Nunavik in Northern Quebec. A focused ethnographic approach was adopted. Inuit women of Nunavik participated in individual or group interviews during which a semi-structured interview guide was used to determine their perceptions of the barriers and facilitators to implementing HPV self-sampling as a primary screening strategy for cervical cancer. The data were analysed based on Paillé's grounded theory of qualitative analysis. Twenty-eight Inuit women participated in this study. Analysis revealed five subcategories of facilitators and four barriers. Inuit women may embrace the self-sampling method. Importantly, in order to be effective, these strategies must be culturally sensitive and adapted to women's preferences so as to increase sustainability. The results of this study provide the means for integrating the perspectives of Inuit women in implementing HPV self-sampling as a primary screening strategy for cervical cancer in Nunavik. Consideration of these facilitators and barriers might maximise the chance of success and optimise the screening participation rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".