Reaching under‐screened/never‐screened indigenous peoples with human papilloma virus self‐testing: A community‐based cluster randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Indigenous women in the high-income countries of Canada, Australia, New Zealand and USA, have a higher incidence and mortality from cervical cancer than non-Indigenous women. Increasing cervical screening coverage could ultimately decrease cervical cancer disparities. AIMS: To increase cervical screening for under-screened/never-screened Māori women. MATERIALS AND METHODS: This study was a cluster randomised controlled trial. Inclusion criteria were women aged 25-69, last screened ≥4 years ago, in Northland, New Zealand. The intervention arm was the offer of a human papilloma virus (HPV) self-test and the control arm was the usual offer of standard care - a cervical smear. The primary outcome was rate of cervical screening in the intervention group compared to control in Māori, the Indigenous peoples of New Zealand. Six primary care clinics were randomly allocated to intervention or control. RESULTS: Of 500 eligible Māori women in the intervention arm, 295 (59.0%) were screened. Of 431 eligible Māori women in the control arm, 94 (21.8%) were screened. Adjusting for age, time since last screen, deprivation index, Māori women in the intervention arm were 2.8 times more likely to be screened than women in the control arm (95% CI: 2.4-3.1, P-value <0.0001). CONCLUSIONS: Offer of HPV self-testing could potentially halve the number of under-screened/never-screened Māori women and decrease cervical morbidity and mortality. These results may be generalisable to benefit Indigenous peoples facing similar barriers in other high-income countries.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".