Aotearoa New Zealand Deaf women’s perspectives on breast and cervical cancer screening
Bibliographic record
Abstract
AIMS: Since the introduction of both cervical and breast screening programmes in Aotearoa New Zealand, mortality rates have dropped. Both screening programmes track women's engagement, but neither capture the level of engagement of Deaf women who are New Zealand Sign Language users or their experiences in these screening programmes. Our paper addresses this knowledge deficit and provides insights that will benefit health practitioners when providing screening services to Deaf women. METHODS: We used qualitative interpretive descriptive methodology to investigate the experiences of Deaf women who are New Zealand Sign Language users. A total of 18 self-identified Deaf women were recruited to the study through advertisements in key Auckland Deaf organisations. The focus group interviews were audiotaped and transcribed. The data was then analysed using thematic analysis. RESULTS: Our analysis indicated that a woman's first screening experience may be made more comfortable when staff are Deaf aware and a New Zealand Sign Language interpreter is used. Our findings also showed that when an interpreter is present, extra time is required for effective communication, and that the woman's privacy needs to be ensured. CONCLUSION: This paper provides insights, as well as some communication guidelines and strategies, which may be useful to health providers when engaging with Deaf women who use New Zealand Sign Language to communicate. The use of New Zealand Sign Language interpreters in health settings is regarded as best practice, however their presence needs to be negotiated with each woman.
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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 | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".