Reducing disease burden and health inequalities arising from chronic disease among indigenous children: an early childhood caries intervention in Aotearoa/New Zealand
Bibliographic record
Abstract
BACKGROUND: Maaori are the Indigenous people of New Zealand and do not enjoy the same oral health status as the non-Indigenous majority. To overcome oral health disparities, the life course approach affords a valid foundation on which to develop a process that will contribute to the protection of the oral health of young infants. The key to this process is the support that could be provided to the parents or care givers of Maaori infants during the pregnancy of the mother and the early years of the child. This study seeks to determine whether implementing a kaupapa Maaori (Maaori philosophical viewpoint) in an early childhood caries (ECC) intervention reduces dental disease burden among Maaori children. The intervention consists of four approaches to prevent early childhood caries: dental care provided during pregnancy, fluoride varnish application to the teeth of children, motivational interviewing, and anticipatory guidance. METHODS/DESIGN: The participants are Maaori women who are expecting a child and who reside within the Maaori tribal area of Waikato-Tainui.This randomised-control trial will be undertaken utilising the principles of kaupapa Maaori research, which encompasses Maaori leadership, Maaori relationships, Maaori customary practices, etiquette and protocol. Participants will be monitored through clinical and self-reported information collected throughout the ECC intervention. Self-report information will be collected in a baseline questionnaire during pregnancy and when children are aged 24 and 36 months. Clinical oral health data will be collected during standardised examinations at ages 24 and 36 months by calibrated dental professionals. All participants receive the ECC intervention benefits, with the intervention delayed by 24 months for participants who are randomised to the control-delayed arm. DISCUSSION: The development and evaluation of oral health interventions may produce evidence that supports the application of the principles of kaupapa Maaori research in the research processes. This study will assess an ECC intervention which could provide a meaningful approach for Maaori for the protection and maintenance of oral health for Maaori children and their family, thus reducing oral health disparities. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12611000111976.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".