Moving research knowledge into dental hygiene practice.
Bibliographic record
Abstract
Dental hygiene, as an emerging profession, needs to increase the number of intervention studies that identify improvements in oral health outcomes for clients. Historically, dental hygiene studies have typically been atheoretical, but the use of theoretical frameworks to guide these studies will increase their meaningfulness. Rogers' theory of diffusion of innovations has been used to study research utilization across many disciplines, and may offer insights to the study of research use in dental hygiene. Research use is an important component of evidence-based practice (EBP), and diffusion of research knowledge is an important process in implementing EBP. The purpose of this paper is to use diffusion of innovations theory to examine knowledge movement in dental hygiene, specifically through the example of the preventive practice of oral cancer screening by dental hygienists, considered as an innovation. Diffusion is considered to be the process by which an innovation moves through communication channels over time among a social network. We suggest diffusion theory holds promise for the study of knowledge movement in dental hygiene, but there are limitations including access to and understanding research studies as innovations. Nevertheless, using a theoretical framework such as Rogers' diffusion of innovations will strengthen the quality of intervention research in dental hygiene, and subsequently, health outcomes for clients.
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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 | 0.045 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".