Perceptions and attitudes of Canadian dentists toward digital and electronic technologies.
Bibliographic record
Abstract
OBJECTIVES: To determine dentists" perceptions of the usefulness of digital technologies in improving dental practice and resolving practice issues; to determine dentists" willingness to use digital and electronic technologies; to determine perceived obstacles to the use of digital and electronic technologies in dental offices; and to determine dentists" attitudes toward Internet privacy issues. METHODS: An anonymous, self-administered survey of Canadian dentists was conducted by mail. A potential mailing list of 14,052 active Canadian dentists was compiled from the 2003 records of provincial regulatory bodies. For each province, 7.8% of the dentists were randomly selected with the help of computer software. The surveys were mailed to this stratified random sample of 1,096 dentists. RESULTS: The response rate was 28% (312/1,096). Of the 312 respondents, 4 (1%) were in full-time academic positions, 16 (5%) were not practising, and 9 (3%) provided incomplete data. Therefore, 283 survey responses were available for analysis. More than 60% of the dentists indicated that computer technology was quite capable or very capable of improving their current practice by increasing patient satisfaction, decreasing office expenses, increasing practice efficiency, increasing practice production, improving record quality and improving case diagnosis and treatment planning. More than 50% of respondents reported that digital photography and digital radiography were quite useful or very useful. About 70% of the dentists agreed or strongly agreed with using digital and electronic technologies to consult with dental specialists. Cost of equipment and lack of comfort with technology were regarded as significant or insurmountable obstacles by substantial proportions of respondents. CONCLUSIONS: Respondents generally viewed digital and electronic technologies as useful to the profession. Increased office efficiency and production were perceived as positive effects of digital and electronic technologies. These technologies are more often used for consulting with colleagues rather than for consulting with patients. The major obstacles to the general use of these technologies were related to cost, lack of comfort with technology and differences in legislation between provinces and countries. Privacy issues were not perceived as a significant barrier.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".