Understanding the volunteer motivations, barriers and experiences of urban and rural youth: a mixed-methods analysis
Bibliographic record
Abstract
There is a growing need to promote volunteerism among youth, given the declining rates across Western countries, and the societal and individual benefits gained through community engagement. Research has focused on individual predictors of volunteerism, but little is known about the role of context, such as urban–rural differences when examining comparable cohorts. Using data from a Canadian survey and semi-structured interviews, we documented differences in volunteer motivations and barriers between urban and rural youth. Survey results showed that rural youth volunteered more hours if they had friends who volunteered, whereas urban youth invested more hours if they were motivated to explore their strengths. Qualitative findings highlighted the importance of networks as levers to formal and informal volunteering, especially for rural youth, and the unique social and structural barriers related to volunteerism depending on place of residence. Contextual factors should be considered when designing strategies to recruit and retain young volunteers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".