“I was giving someone who didn’t have a voice a voice”: exploring qualitative mini-research projects as a tool to teach students about aging
Bibliographic record
Abstract
In the study of gerontology, fieldwork with older adults is often used to enhance students’ understanding of the aging process. While assignments based on interactions with older adults are a common practice in teaching students enrolled in gerontology studies, we know less about the impact of such activities on students from other disciplines. This paper summarizes students’ experiences with an assignment offered to a diverse cohort of undergraduate students who took a course in social gerontology. To complete this assignment, students had to interview an older adult, summarize the life story of the participant, apply a theoretical perspective to the older adult’s life story, and reflect on the process. Analyzing data derived from 72 assignments and 10 semi-structured interviews with students who were enrolled in the course, this paper examines students’ experiences with this assignment. Specifically, we identify what aspects of the assignment students found beneficial, what aspects they found challenging, and in what ways this assignment helped students to enhance their understanding of aging. Our findings suggest that students found it challenging to recruit an older adult for an interview and struggled with the semi-structured nature of the interview process. All students found the actual interview process to be extremely rewarding and beneficial for their learning. In discussion, we provide some recommendations on how to offer this type of assignment to a diverse group of students enrolling in the courses on social gerontology.
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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 | 0.080 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".