Development of evidence-informed recommendations for the training and management of disability sport event volunteers: an application of the AGREE II instrument
Bibliographic record
Abstract
Purpose This paper describes how the Appraisal of Guidelines Research and Evaluation II (AGREE II) instrument was operationalized to systematically develop evidence-informed recommendations for training and managing volunteers for disability sport events. Design/methodology/approach A combination of methods was used to develop the recommendations, including (1) a review of existing volunteer training resources, (2) a rapid evidence review of the literature and (3) interviews with athletes (n = 5), volunteers (n = 14) and sport event organizers (n = 5). An expert consensus panel (n = 12) then reviewed and deliberated the evidence to formulate the recommendations. Additional feedback was solicited from external knowledge reviewers (n = 51) within the disability sport community. Findings Twelve key recommendations were developed, each with detailed parameters specifying who, when, how and what information to include when implementing the recommendations. These recommendations represent an important step toward the harmonization of volunteer training and management practices among Canadian disability sport organizations and events. Originality/value The process used in this paper can help advance collaboration between sport practitioners and researchers and assist with the development of practical recommendations in the disability sport and event sector.
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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 | 0.557 | 0.657 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".