Enhancing Nursing Students’ Understanding of Oral Health: An Educational Intervention with an Interprofessional Component
Bibliographic record
Abstract
Oral health is integral to general health and essential for well-being, and therefore, should be prioritized in pediatric nursing education. The purpose of this pilot study was to examine if an oral health education intervention with an Interprofessional Education (IPE) component delivered to third-year baccalaureate nursing students would improve their knowledge of pediatric oral health care. Nursing students (n=99) from a Bachelor of Nursing program in a mid-Western Canadian university completed a survey before and after receiving the educational intervention which included a two-hour lecture from a Dentistry faculty member and a one-hour clinical lab in which nursing students learned how to conduct a comprehensive oral health assessment in practice. Paired-sample t-tests were conducted to compare pre-and post-intervention survey scores. Findings indicate a statistically significant (p < .001) increase in knowledge from pre-test (67%) to post-test (86%) and contribute to a new understanding of the importance of pediatric oral health care in nursing education. The outcome of this intervention is that registered nurses can be prepared with the knowledge necessary to address the disparate oral health challenges experienced by children globally. These findings will provide the foundation for the refinement and implementation of the educational intervention on an international, multi-site scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".