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Record W2129023391

Should dental hygienists replace dental directors in screening high-needs children?

2005· article· en· W2129023391 on OpenAlexaffabout
Elizabeth Rolland

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineFamily medicineDental careDentistryDental health
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.022
GPT teacher head0.270
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2005
Admission routes2
Has abstractyes

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