Experiences of Volunteering: A Qualitative Study of Intergenerational Volunteerism in the Snow Buddies Program and Similar Volunteer Initiatives in Niagara
Bibliographic record
Abstract
Snow Buddies is an intergenerational volunteer program in the Niagara Region that recruits’ youth volunteers to remove snow and ice from the driveways and walkways of older adult clients with disabilities. Research demonstrates that involvement in intergenerational volunteer programs between older adults and youth can benefit both groups through building communication skills and new relationships (Blais et al., 2017). There is a gap in the literature addressing intergenerational volunteerism between youth and older adults outside of specific contexts, such as long-term care homes (Hickey et al., 2004; Kim & Lee, 2018; Santini et al., 2018). As such, this qualitative research explores youth and older adults’ experience of being a part of the Snow Buddies program and similar volunteer initiatives in general and throughout unique times such as the COVID-19 pandemic. Youth volunteers (ages 14-25), older adult clients (76-87) and one older adults’ family member participated in semi-structured interviews to share their experiences of being a part of the Snow Buddies program or a similar volunteer initiative. Data were gathered from 14 participants: 9 volunteers (55% female), 4 clients, and 1 family member. Reflexive thematic analysis was used to code and analyze all interview transcripts. Participants reported that the Snow Buddies program and similar volunteer initiatives created a sense of belonging through an intergenerational connection, as well as a sense of personal fulfillment for volunteers. They also reported that the program was physically and socially challenging at times for volunteers and clients, including during the COVID-19 pandemic. Findings from this research have program related implications for perceived benefits of an intergenerational volunteer program between youth and older adults, due to its unique study design and participant sample. As well, there are risk mitigation implications for volunteering during a unique time such as the COVID-19 pandemic.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".