Mass Gatherings and Youth Peer Volunteerism
Bibliographic record
Abstract
Introduction: Music and sporting events are mass gatherings with unique risks related to participation. “All-ages” events, which include participants below the age of majority (18 in many jurisdictions), have been observed to have an over-representation of patient presentations in the youth category. Peer helpers may lower the barrier to seeking on-site care. Youth (peer-aged) volunteerism provides opportunities for exposure to new environments, skills, and mentorship. Medical volunteerism may promote personal satisfaction through prosocial behavior (i.e., helping others), community engagement and immersion into a potential health professions career path. Methods: We conducted an observational pilot feasibility study with feedback forms and semi-structured interviews. The pilot program paired youth with parents/guardians/responsible adults as health care volunteers at special events. Results: Youth/adult dyads volunteered for a variety of events in Canada during the 2018 event season. All participants in the “Juniors Program” completed at least a Standard First Aid course, including orientation to personal safety and confidentiality. Each pair worked in one of two areas: first aid or Festival Health (the harm reduction space at music events) providing peer-to-peer and “all-ages” support. Post-event feedback from the dyads revealed many positive experiences and universally called for more opportunities. Discussion: A strong volunteer base is an asset to any community. In this pilot study, the volunteer experiences were supervised by a team of credentialed health care professionals. The authors report on qualitative feedback in themes based on patient perspective, volunteer perspective, team perspective, and event management perspective. More research is needed to measure the outcomes of the Junior’s Program. More Investigation is needed to determine not only the long-term benefits of participation on event medical teams, but also to identify factors that shape a positive experience for youth, their parents, and the event participants that they support.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".