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Record W3021113064 · doi:10.1080/03601277.2020.1754355

“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

2020· article· en· W3021113064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducational Gerontology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQualitative researchPsychologyPedagogyMedical educationS VoiceMedicineSociologyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.043
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.795
GPT teacher head0.699
Teacher spread0.096 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it