Dental students’ reflective learning from a geriatric interview assignment
Bibliographic record
Abstract
OBJECTIVES: As part of geriatric education at the University of British Columbia's Faculty of Dentistry, undergraduate students are required to interview an older adult over 65 years old and critically reflect in writing on the meaning of this interview to themselves and their professional lives in not more than 2000 words. They are also encouraged to use a life grid. This study explored the impact of this assignment on the students as learners and on their views about their profession. METHODS: Interview assignments were collected from the entire cohort of 54 third-year students in 2021 and analyzed in 5 stages, using an exploratory thematic analysis, including an interactive coding process to identify patterns (themes) within the assignments using NVivo R1 software. Two researchers coded assignments individually and met to reach a consensus about the codes, to mitigate potential biases. RESULTS: Five main themes were identified, including communication, life course journey, person-centered care, social determinants of health, and access to care. A wide range of ideas emerged under each theme, including several practical suggestions to improve future practice as an oral health professional. A little over 40% of the students used the life grid in their interviews. Modifications on the interview assignment are suggested. CONCLUSION: Students' reflections highlighted their observations on a wide range of ideas within each theme, many pertaining to their future profession. They also discussed how this knowledge would inform their future practice in terms of their interactions with, and providing care to, patients with similar situations.
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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 | 0.032 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".