Ontario Dentists' Practice of Sedation and General Anesthesia: Barriers to Access and Use.
Bibliographic record
Abstract
BACKGROUND: To investigate, among Ontario dentists, (1) self-reported barriers to access to sedation and general anesthesia (GA) services and (2) their current use of sedation and GA. METHODS: Of Ontario dentists practising, 3001 were randomly selected to complete a 16-question survey by mail or online in 2011. Mixed analysis of variance (ANOVA) followed by independent-sample t tests or 1-way ANOVA evaluated the relation between dentists' views and demographic variables including sex, clinical experience and size of primary practice. RESULTS: Of the participants (n = 1076; 37.9% response rate), 69.7% were male, 84.4% were general practitioners, mean time in practice was 20.6 years (0.5-42 years) and 42.2% were in cities of over 500 000 people. Most dentists (60.2%) provided anesthesia services, although 38.2% indicated lack of training and the belief that there is no patient demand (25.3%) as reasons not to use anesthesia in their offices. Nitrous oxide was used 17.5% of the time for all dental procedures except implants. Barriers to referral of patients for anesthesia services included high costs associated with sedation/GA (72.2%) and patient fear of anesthesia (33.5%). CONCLUSION: This study identified a perceived lack of patient demand, lack of dentist training, high costs of sedation/GA and patient fear of sedation/GA as primary barriers to use of sedation/GA in Ontario dental practices. The use of various anesthesia modalities is diverse, with 60.2% of dentists providing sedation/GA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".