Implicit ageism in dental students: general representations of ageing health and specific representations of the mouth
Bibliographic record
Abstract
By 2040, more than one in four Europeans is expected to be over 65. Despite improvements in older patients’ care, oral healthcare is still neglected. One of the major obstacles could be the negative representations associated with aging: ageism. The present study aims to quantify and qualify ageism for general and specific representations of aging associated with the mouth of an older patient compared to a young patient in dental students. Undergraduate students at the French dental school of Clermont-Ferrand were invited to participate in the study. Ageism was quantified by asking the students to estimate how many older adults have some negative conditions, which were then compared to real data. Representations of the mouth of a young vs. an older adult were collected by asking each student to write the first five words that came to their mind when they thought about the mouth of a young person and then the mouth of an older person. The students exhibited a large overestimation of health problems in the older adult population. The words given for a young adult were positive 49% of the time (vs. 23% negative), whereas 69% of the words were negative for an older adult (vs. 8% positive). The students in the second year were less negative than students in higher years. Our study contributes to assessing how dental students could exhibit implicit ageism. They show very negative representations of aging from the beginning of their training, which get even worse after they are exposed to clinical training with older patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".