Supply of care by dental therapists and emergency dental consultations in Alaska native communities in the Yukon-Kuskokwim delta: a mixed methods evaluation.
Bibliographic record
Abstract
OBJECTIVES: Examine the relationship between supply of care provided by dental therapists and emergency dental consultations in Alaska Native communities. METHODS: Explanatory sequential mixed-methods study using Alaska Medicaid and electronic health record (EHR) data from the Yukon-Kuskokwim Health Corporation (YKHC), and interview data from six Alaska Native communities. From the Medicaid data, we estimated community-level dental therapy treatment days and from the EHR data we identified emergency dental consultations. We calculated Spearman partial correlation coefficients and ran confounder-adjusted models for children and adults. Interview data collected from YKHC providers (N=16) and community members (N=125) were content analysed. The quantitative and qualitative data were integrated through connecting. Results were visualized with a joint display. RESULTS: There were significant negative correlations between dental therapy treatment days and emergency dental consultations for children (partial rank correlation = -0.48; p⟨0.001) and for adults (partial rank correlation = -0.18; p=0.03). Six pediatric themes emerged: child-focused health priorities; school-based dental programs; oral health education and preventive behaviors; dental care availability; healthier teeth; and satisfaction with care. There were four adult themes: satisfaction with care; adults as a lower priority; difficulties getting appointments; and limited scope of practice of dental therapy. CONCLUSIONS: Alaska Native children, and to a lesser extent adults, in communities served more intensively by dental therapists have benefitted. There are high levels of unmet dental need as evidenced by high emergency dental consultation rates. Future research should identify ways to address unmet dental needs, especially for adults.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".