Dental Students’ Positive and Negative Views on Aging at an Undergraduate Course at the University of British Columbia, Canada
Bibliographic record
Abstract
Background: Different views of aging exist, including ageism as the stereotyped idea about older adults in general. The objective of this study was to perform an initial exploration on how third-year undergraduate dental students at the University of British Columbia (UBC), Vancouver, Canada self-perceived themselves at ages 65, 75, or 85 years old. Methods: For a period of 10 years, and using a cross-sectional design, third-year undergraduate dental students were asked to envision themselves at 65, 75, or 85 years old in a brief 150-word written essay. We employed a thematic analysis of the textual data via a coding process as suggested. The main themes were identified and displayed in a table with their respective categories of information. NVivo® 12 (QSR International) software was used for data analysis. Results: Between 2010 and 2020, a total of 519 students enrolled in the UBC undergraduate dental program; 425 essays were collected. Initial coding and thematic analysis led to the identification of four main themes and eight categories; themes included "Ups and Downs," "Financial Sustainability," while categories focused on "Trade-offs," and "Camaraderie," for example. Different levels of ageism were also apparent when students saw themselves experiencing isolation and forgetfulness regardless of the selected age and the academic year. For other students, they anticipate aging would be healthy, joyful, and productive. Conclusion: Ageism was present when students imagined themselves at three different older ages, but so were more realistic views of an aging. Further studies are warranted to unravel the impact of geriatric education in tackling stereotypes and 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 | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".