Should dental hygienists replace dental directors in screening high-needs children?
Bibliographic record
Abstract
PURPOSE: The purpose of this research was to determine whether dental hygienists are as effective as dental directors in screening high-needs children who require emergency care. METHODS: In 2000, the Community Dentistry Health Services Research Unit (CDHSRU) at the University of Toronto completed a prospective cohort study to determine whether care proposed by dental directors exposed to evidence-based practices was significantly different from the care provided by dental hygienists who screened children enrolled in the provincially mandated Children in Need of Treatment (CINOT) program. RESULTS: The dental directors and dental hygienists each prepared a treatment plan for the 71 children enrolled in this study. These plans were analyzed using a paired t-test model after being translated into relative value units (RVU). It was determined that there was no statistically significant difference between the overall dental treatment proposed by the dental hygienists and the treatment proposed by the dental directors (p=.749). A similar analysis stratified by subject site and by service type also showed no significant differences. CONCLUSIONS: The results suggest that dental hygienists are equally as effective as dental directors in screening high-needs children and may be capable of assuming the role of first point of contact for children within high-need dental programs.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".