Perceptions about Aging and Ageism from 14 Cross-sectional Cohorts of Undergraduate Dental Students
Bibliographic record
Abstract
BACKGROUND: Although positive and negative views of aging and older adults exist, how undergraduate dental students imagine their lives to be as they grow older remains to be fully explored. This study aimed at determining the self-perceived views of being 65, 75, or 85 y of age, as expressed by undergraduate dental students at the University of British Columbia (UBC), Vancouver, Canada. METHODS: A 14-y cross-sectional study design was utilized involving third-year undergraduate dental students at UBC's Faculty of Dentistry. Brief individual essays (150 words) encompassing students' self-perceived views were gathered as part of a dental geriatric course from 2009 to 2022; however, essays were not mandatory. Saldaña's inductive coding and thematic analysis of textual data were used. Themes and categories of information were identified and matched with their excerpts while aiming for data saturation. RESULTS: Over the 14-y period, 657 students were enrolled in UBC's undergraduate dental geriatric course, and 561 essays were collected. Inductive coding and thematic analysis identified 5 main themes and 11 categories. While themes included "oral health, general health, and the mind" and "me, myself, and familial relationships," the categories focused on "(un)able bodies" and "general health." Positive views about the aging process were shared, while less optimistic ideas-and even ageism-were apparent when students saw themselves as not employable or living in isolation. Positive and negative views were not bound by the students' academic year but might have been influenced by the COVID-19 pandemic. CONCLUSION: Although the number of older adults already surpasses the number of children in many countries, ageism appears to have permeated through students' views of 3 older ages. More positive yet realistic views of growing older were also shared. Follow-up studies are needed to explore the impact of dental education on decreasing ageism. KNOWLEDGE TRANSFER STATEMENT: As the proportion of older adults in the global population steadily grows, it is important to educate heath care providers about normal and pathologic aging to avoid ageism-stereotypes, prejudices, and discrimination against older adults. This cross-sectional study involved 14 cohorts of undergraduate dental students exploring their self-perceived views of growing older. Although positive and negative views of aging were shared, dental education must focus on decreasing ageism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".