Types of Canadian Dentists Who Are More Likely to Provide Dental Implant Treatment
Bibliographic record
Abstract
OBJECTIVE: We designed to determine the variables that influence the adoption rate of implant technology amongst Canadian dentists. METHODS AND MATERIAL: In this cross-sectional study, an anonymous survey questionnaire was sent to all licensed Canadian dentists, both general practitioners and specialists. A 3-part questionnaire accompanied by a postage prepaid envelope was sent to all licensed Canadian dentists. No second mailing was performed. The plan was to measure the effects of age, gender, language, type of specialties, ownership, association with other dentists, and the location of practice on the adoption of dental implant technology. RESULTS: The multivariate regression analyses indicate that the dentists' gender, province of practice, specialty, and whether they practice alone or in association with other practitioners are significant factors associated with the adoption of implant technology in providing both surgical and prosthetic aspects of implant therapy. Female dentists provided significantly less implant prostheses than their male counterparts (OR: 1.75, P < 0.05). Canadian dentists in Atlantic regions were significantly less likely than those in other provinces to surgically place an implant or restore implant prostheses (OR: 0.34, OR: 0.30). In addition, those dentists who owned their practices were 2.35 (P < 0.05) times more likely to provide implant prostheses. CONCLUSIONS: This study provides an evidence that the rate of adoption of implant technology among Canadian dentists depends mainly on practitioners' age, practice ownership, and their specialties.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".