TEACHING GERONTOLOGY TO SOCIAL WORK STUDENTS: APPLYING THE EXPERIENTIAL LEARNING USING ETHNOGRAPHIC INTERVIEW
Bibliographic record
Abstract
Abstract There is an increasing need for well-trained social workers to support the growing aging population in Canada. Still, concerns arise regarding social work students’ insufficient knowledge and understanding of aging and aging-related issues. This study aims to examine social work students’ experience when experiential learning through ethnographic interview with older adults is applied as a pedagogical approach. This study was conducted based on two cohorts of social work undergraduate students who enrolled in a gerontology course in a Canadian university between 2020 and 2021. Students conducted an ethnographic interview with older adults aged 70 years and older and wrote a reflection paper as an assignment. We did a thematic analysis of eight reflection papers in which consent was obtained from students. We find that students connect aging-related theories/models to various topics discussed during their ethnographic interview, reflect on their personal experiences with aging family members, and show a positive perception of aging and attitude towards working with aging. The findings also suggest the benefit of adopting an approach of experiential learning through the ethnographic interview with older adults to teach gerontology to social work students. We offer recommendations for educators to create opportunities for students, especially from social work or other helping professions who traditionally have shown a lack of interest in working with older adults, to meet and interact with older adults, and to further enhance students’ competencies and interests in the fields of senior care.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".