A critical reflection of an intergenerational, student-led team bringing social robots and research to older adults in the community
Bibliographic record
Abstract
Knowledge translation and exchange to promote the health and well-being of older adults requires collaborative relationships between researchers and knowledge users. Students are uniquely positioned to engage with the community and bridge these science-practice gaps. In this paper, we highlight key lessons learned from our interdisciplinary and intergenerational team's critical reflections on our experiences and learnings bringing the LOVOT social robot to engagement sessions with older adults in our community. Our critical reflection process followed the reflection framework by Rolfe et al. (2001), guided by three questions: (1) "What?", (2) "So what?," and (3) "Now what?" We conducted thematic analysis on our collective reflections. Three key learnings emerged from our critical reflections: (1) the values of meaningful interactions between older adults in our community and our team; (2) the diversity of backgrounds and perspectives of older adults in our community; and (3) factors that supported or challenged our community engagement sessions. We conclude with six recommendations for future student-led community engagement sessions.
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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 | 0.046 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.026 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".