Learning Together During a Pandemic Lockdown: Connecting Older Mentors with Nursing Students
Bibliographic record
Abstract
Abstract Nursing education tends to focus on complex clinical issues affecting older adults who are acutely ill or in long-term care. This creates challenges for educators wanting to expose students to a greater range of experience, including realities of healthy aging. Opportunities to do things differently were presented when an established undergraduate nursing course on complex aging care underwent significant adjustment in the early months of the COVID-19 pandemic. As the course was condensed and moved online and clinical sites closed, invitations were extended to community-dwelling older people who wanted to “help teach nursing students about aging”. The response was overwhelming; over nine days, 118 people (ages 65-94) volunteered to be mentors. Through weekly online/ phone conversations, each person guided their assigned student to learn about diverse experiences of aging. Post-survey results showed the impact of these conversations. Over 90% of mentors felt they had contributed in a meaningful way to student learning and would do it again and recommend it to others. 85% of students felt it was a meaningful experience, offering comments like: “I am more mindful of my assumptions now” and “I learned to approach interactions with older adults as a collaboration; we have so much to give each other”. These results provide a needed counterpoint to the predominant COVID discourse of older people as “isolated, helpless, and needy”. Students came to understand that older people were also “engaged, active, and contributing” and identified how this had changed their view of aging. Implications for nursing education are explored.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".