Oral health beliefs and behaviors of nurse and nurse practitioner students using the HU-DBI inventory: An opportunity for oral health vicarious learning
Bibliographic record
Abstract
Background: Oral health access to care issues are resulting in curricular changes to train nursing students as oral health educators and providers. However, little data are available concerning their personal oral health beliefs/behaviors. The study purpose was to gather information from nurse and nurse practitioner students regarding their oral health beliefs and behaviors.Methods: Using the Hiroshima University Dental Behavioural Inventory (HU-DBI), survey data were gathered from nurse and nurse practitioner students as well as dental hygiene students as controls concerning their oral health beliefs and behaviors.Results: Mean HU-DBI scores were higher among nurse practitioner than nursing students, indicating more positive beliefs/behaviors, but both were lower than dental hygiene students. Both nurse and nurse practitioner students reported significantly fewer dental visits and some poorer hygiene practices than controls. Additionally, nursing students were more likely to believe that their teeth were worsening despite brushing.Conclusions: Assessment of personal oral health beliefs/behaviors should occur early in nursing education with mentoring so that optimal modeling can positively impact patients’ oral health. Oral health education opportunities within and among disciplines are discussed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".