Reaching under‐screened/never‐screened indigenous peoples with human papilloma virus self‐testing: A community‐based cluster randomised controlled trial
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it