Exploring the acceptance of geriatric dentistry programming for undergraduate dental students through stakeholder interviews
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: Older adults frequently report unmet oral healthcare needs. Current research suggests a lack of provider willingness to perform geriatric dental care plays a role in limiting older adults' access to dental services. To better understand the acceptance of geriatric dentistry programming in Ontario, and to explore considerations for successful implementation, we completed consultations with dental students and dental education stakeholders. Findings from a scoping review we conducted previously (Alicia C. Brandt and Cecilia S. Dong) were used to guide this research. METHODS: Consultations involved a questionnaire and semi-structured individual interviews. Descriptive and parametric statistics such as Pearson's bivariate correlation and One-way analysis of variance were completed on questionnaire data using SPSS V.28. Interview data were transcribed verbatim, and the content was analyzed using emergent coding and thematic analysis in NVivo. Student and faculty data were analyzed separately and then consolidated. RESULTS: Ten students and 12 dental faculty members completed the questionnaire of which ten students and nine faculty members also participated in interviews. Themes were organized into barriers and facilitators, with a subsection on interprofessional collaboration. Barriers included: 1. Student anxiety and skill level; 2. Constraints of the learning environment; 3. Patient factors; and 4. Knowledge gaps. Facilitators included: 1. Learning environment and culture; 2. Volume of exposure; 3. Soft skills; and 4. Desired interventions. CONCLUSIONS: Both students and faculty stakeholders demonstrated acceptance of geriatric dentistry programming at the undergraduate dentistry level that supports improved access to care for this population. Pilot programs integrating different intervention elements which were viewed as most promising would be beneficial.
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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 | 0.039 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".