Achieving cervical cancer elimination among Indigenous women
Bibliographic record
Abstract
Achieving the World Health Organisation (WHO) cervical cancer elimination target of fewer than four new cases per 100,000 woman-years requires scaling up HPV vaccination of girls, cervical screening, and pre-cancer and cancer treatment. We reviewed data from four high-income colonised countries (Australia, Canada, Aotearoa New Zealand (NZ), and the United States (US)) to identify how each is currently performing compared to the cervical cancer incidence elimination and triple-intervention targets, nationally and in Indigenous women. We also summarise barriers and enablers to meeting targets for Indigenous women. To achieve elimination, cervical cancer incidence must be reduced by 74% in Indigenous women in Australia, and 63% in Maori women in NZ; data were not published in sufficient detail to compare incidence in Indigenous women in Canada or the US to the WHO target. Only Australia meets the vaccination coverage target, but uptake appears comparatively equitable within Australia, NZ and the US, whereas there appears to be a substantial gap in Canada. Screening coverage is lower for Indigenous women in all four countries though the differential varies by country. Currently, only Australia universally offers HPV-based screening. Data on pre-cancer and cancer treatment were limited in all countries. Large inequities in cervical cancer currently exist for Indigenous peoples in Australia, Canada, New Zealand and the US, and elimination is not on track for all women in these countries. Current data gaps hinder improvements. These countries must urgently address their systemic failure to care and provide health care for Indigenous women.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".