Fuzzy cognitive mapping with Inuit women: what needs to change to improve cervical cancer screening in Nunavik, northern Quebec?
Bibliographic record
Abstract
BACKGROUND: Among Canadian Inuit, cervical cancer incidence and mortality rates are up to three times higher than the Canadian average. Cervical cancer is preventable through regular screening which, in Quebec, is opportunistic and requires physical examination and Papanicolaou ("Pap") smears. Since Human Papillomavirus (HPV) is the necessary cause of cervical cancer, HPV testing is a plausible screening alternative. HPV testing by self-sampling also addresses several barriers associated with physical examination and access to healthcare. In a participatory research paradigm, we worked with two communities of Nunavik to explore the possible implementation of HPV self-sampling. METHOD: Key community stakeholders formed an Advisory Committee to guide direct discussions with Inuit women. We presented available facts around cervical cancer, HPV and the female anatomy, and used Fuzzy Cognitive Mapping to collate women's views. A thematic analysis summarized data, adding links and weights to represent the relationship of each factor on the outcome: screening for cervical cancer. RESULTS: According to the 27 Inuit women who participated, the most influential factor in using health services was the cultural awareness of the healthcare provider. A significant barrier to screening was patient lack of information. The principal vector of change - the factor most likely to influence other factors - was the means of communication between the healthcare provider and the patient: visual communication was told to be the most effective. CONCLUSION: Fuzzy Cognitive Mapping is a practical tool for discussing possible health actions with stakeholders and to inform future research. The tool offers a visual aid for discussion across cultural and educational differences. It can help to build the partnerships that incorporate community voices into co-design of interventions that are relevant to and aligned with the needs of those who use them.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".