Intergenerational Benefits of Student Volunteerism in Medical Education
Bibliographic record
Abstract
As many medical schools have canceled summer electives, research positions, and clinical rotations during the COVID-19 pandemic, students have faced significant disruption of daily routines, with associated uncertainty and social withdrawal. Such loss of routine academic opportunities and social interactions may lead to feelings of inertia and anxiety, increasing the already high mental health burden experienced by students and worsening preexisting mental health conditions. 1 One solution is volunteerism. We founded Creative Connection to connect students with seniors across Canada via video call to provide live one-on-one and small-group musical/art performances. Through these interactions, students regain self-esteem and purpose in a social role, which combats feelings of helplessness and predicts better mental health. 2 Furthermore, these intergenerational interactions promote positive attitudes regarding aging and patient-centered care—essential for cultivating empathy in future physicians during a time of limited patient interactions. Not only do intergenerational initiatives restore a sense of purpose for students, but they also mobilize the workforce necessary to help health care staff provide social connection for seniors.
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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".