Revved up: The influence of volunteer experience on career path
Bibliographic record
Abstract
Benefits of volunteering alongside persons with disabilities include enhanced problem-solving skills and the ability to adapt in various situations; however, little is known about how these volunteer experiences influence volunteers' career paths. Revved Up, a community-based assisted exercise program for persons with disabilities in Kingston, Ontario, integrates program members with student volunteers from Queen's University. The purpose of this study was to retrospectively examine the experiences of former Revved Up volunteers to explore how their experiences may have influenced their future career decisions and pursuits. Hour-long telephone interviews, whereby the interview guide was developed using were conducted with 12 former Revved Up volunteers. A life course perspective was taken to inform the interview and examine how experiences with Revved Up informed trajectories within one's career. Interviews were transcribed verbatim and subjected to dialogical narrative analysis. Three distinct narrative types were identified, each of which demonstrated differential career trajectories, with Revved Up having a varying degree of influence on the volunteers' career path. The core of each narrative type was shaped by the specific career fulfillment being sought by individuals (e.g., desire to have one-on-one meaningful connections with patients or clients vs. desire to affect and see change in patients' or clients' health). These narratives offer a unique understanding of how a physical activity program context is able to facilitate a purposeful volunteer experience which in turn can influence volunteers' decisions and pursuits that relate to one's career trajectory.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".