Factors Influencing Adoption of New Technologies into Dental Practice
Bibliographic record
Abstract
The objective of this study was to explore factors affecting decisions to adopt new technologies into dental practice using a colorimetric rinse test for detection of periodontal disease as a model. Focus groups with key informants in Canadian dentistry and dental hygiene were conducted. A deductive approach used Rogers's diffusion of innovation theory as a framework for organizing codes and subcodes. Two members of the research team independently reviewed and analyzed the data using NVivo 10. The attributes of the technology itself emerged as primary influencers. Perceived relative advantages of the diagnostic mouth rinse over existing methods were potential time efficiency, low implementation cost, and utility of the tool. Low complexity, compatibility with existing routines/beliefs, and the potential for reinvention-the use of a technology for other than its intended purpose (i.e., patient education, monitoring of disease, screening tool in nondental settings)-were other important features enhancing adoption. An overarching concern was that any new technology benefit the patient. Contextual factors also play a role. Numerous communication channels, including opinion leaders, patients, marketing, continuing education courses, and strength of evidence, influenced clinicians, with peer interaction being a stronger influence than marketing. Similar themes arose from specialist, general dentist, and dental hygienist focus groups. Adopter characteristics also came into play: participants ranged in their self-reported innovativeness with many considering themselves "early adopters" of new technology. Findings of this study suggest that the innovation adoption process is not straightforward, but attributes of the innovation, contextual factors, and adopter characteristics play important roles in the process. Knowledge Transfer Statement: Various factors affect the adoption of new tools into clinical dental practice. These include attributes of the test or tool itself, the context of the settings in which the tool is introduced to practitioners, and the characteristics of the clinicians themselves. A qualitative study of dentists and dental hygienists investigated these factors. Situations in which dentists and hygienists interact with their peers and colleagues-through social networks, continuing education courses, conventions, or personal contact-were a major driver in the decision to adopt new technologies. However, even among "early adopters," most were reluctant to use new tests or tools unless they perceived a benefit to their patients or practice.
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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 | 0.010 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".