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Medical Practitioners’ Major Multisport Event Volunteering Experiences

2025· article· en· W4415600678 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEvent Management · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of WaterlooWestern University
Fundersnot available
KeywordsEvent (particle physics)Identification (biology)CohortWork (physics)Medical careComputer-assisted web interviewingMedical costs

Abstract

fetched live from OpenAlex

Medical volunteers are essential to the success of major multisport events (MMSE), yet their experience has not been thoroughly examined. Framed by social exchange theory (SET), this study addresses this gap by examining the perceived benefits and costs of medical volunteers’ MMSE engagement. An online anonymous survey was completed by 78 Canadian medical practitioners who had volunteered at a MMSE in the previous 6 years. Professional identification and networking yet personal inconveniences to family, work, or vacation time were experienced to the greatest extent by the medical volunteers. Professional development and networking were significant positive predictors of the medical volunteers’ future volunteering intentions, yet their medical work at the event was a deterrent to engaging with another MMSE. The medical volunteers’ high reported likelihood of volunteering again likely involves balancing these experiences. The findings highlight several considerations for the effective event management of this critical cohort of volunteers.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.335
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it