Development of Evidence-Informed Recommendations for Training Disability Sport Event Volunteers
Bibliographic record
Abstract
Volunteers are essential for the successful delivery of disability sport events such as the Ontario Para Sport Games. Every year disability sport organizations recruit and train thousands of volunteers to assist with the delivery of both small and large-scale sport events. However, retaining volunteers for future events and ongoing community level programs continues to challenge the disability sport sector. Delivering effective yet enjoyable training to volunteers is a promising avenue to assist in facilitating the retention of volunteers for ongoing community engagement. According to published evidence, volunteer training programs frequently lack the efficient planning and management necessary to produce satisfactory program outcomes. In addition, much of the extant literature related to sport event volunteerism is concentrated on non-disability sport events wherein volunteers’ experiences are investigated as they assume their specific role during the event. As such, there is a dearth of evidence exploring the volunteer experience within the context of disability sport events and pre-event training. Given training is often the first experience volunteers have within an event; it is truly central to the overall volunteer experience. This research, therefore, sought to enhance the volunteer training experience and equip sport administrators with tools to successfully implement a volunteer training program within the context of a disability sport event. The purpose of this thesis is to describe the application of the Appraisal of Guidelines Research and Evaluation II (AGREE II) instrument to formulate evidence-informed recommendations for training disability sport event volunteers. Specially, the AGREE II was used as a systematic guide for the recommendation development process. Methods included (a) review of existing training resources (b) rapid evidence review, (c) qualitative inquiry, and (d) consensus panel deliberations with knowledge users in the disability sport community. Pilot tests with relevant knowledge users were conducted throughout. Results provide specific recommendations to inform the planning and delivery of a volunteer training program to assist in enhancing volunteer experiences and ultimately, the retention of volunteers following a disability sport event. This project provides support for the application of the AGREE II in formulating evidence-informed recommendations for practical use among disability sport organizations.
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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 | 0.202 | 0.541 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".