Self-reported oral health and dental service-use of rangatahi within the rohe of Tainui.
Bibliographic record
Abstract
OBJECTIVES: To investigate the self-reported oral health and use of oral health services by rangatahi (teenagers) residing within the Waikato rohe (region) of the Waikato-Tainui tribal area. METHODS: A cross-sectional study of self-reported oral health and use of dental services by Māori teenagers. The 14-item short-form Oral Health Impact Profile (OHIP-14) was used to collect data on oral-health-related quality of life (OHRQoL). RESULTS: Just over half of the 238 survey participants (who were aged 16 to 18 years old) were male. Most brushed at least once daily. One-quarter reported hiding their smile, and just over one-fifth reported suffering from bad breath. Awareness of their current entitlement to free dental care was high, but it was lower among males. Just over one-third of participants had experienced one or more OHIP-14 impacts; that was higher among females than males, with the largest difference being apparent with the physical disability subscale, where the prevalence of impacts among females was twice that among males. The prevalence of OHIP impacts was higher among those who reported experiencing bad breath often, and significant differences were observed in all seven OHIP domains (as well as in the mean overall OHIP-14 score and in the mean number of different impacts experienced often). CONCLUSIONS: The data provide some important insghts into the oral health perceptions and concerns of young Māori.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".