Are “youth days” effective at motivating new sport participation? Evidence from a pre-post event research design
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the effects that exposure to a youth day event at an elite sport competition has on youth spectators’ motivations to participate in the sport on display. Design/methodology/approach The paper was underpinned by the theory of planned behavior (TPB). Pre- and post-event questionnaires were administered to local grade seven and eight students (n=318) as part of a youth day event at the 2016 Milton International Track Cycling Challenge in Ontario, Canada. Questionnaires assessed each TPB construct one week before the youth day and immediately following the event. Findings The paper provides empirical insights about the shifts from pre- to post-event behavioral antecedent measures. Results suggest youth day events can be effective at driving positive shifts in participation intention and subjective norm among youth populations. Research limitations/implications A control group was not possible as an ethical limitation was created from the school boards which did not allow for some students/classes within the study to not experience the event. Researchers are encouraged to develop a study which allows for a youth control group and assesses the shift in behavioral antecedents at multiple time points post-event. Practical implications The paper includes implications for how to leverage subjective norms as a means of motivating post-event participation. Originality/value The paper fulfils a methodological gap to move beyond cross-sectional data and employ pre-post event research designs to measure the effect spectating an elite sport competition can have on youth’s motivation to participate in the sport on display.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".