Moving stories: Peer athlete mentors' responses to mentee disability and sport narratives
Bibliographic record
Abstract
Past research demonstrates that there are varied responses to the narratives, or stories, that individuals with spinal cord injury (SCI) express. Listeners’ responses to these stories are important as they can have an effect on those who choose to share their stories. Responses are also of interest because individuals with SCI list peers as one of their preferred sources for information about leisure time physical activity. The objective of this study was to explore how peer athlete mentors respond to vignettes based on the stories of individuals with SCI who express a hesitance or resistance to adapted sport. Thirteen peer athlete mentors with SCI from sport and disability organizations participated in hour-long interviews in which four vignettes were discussed. Peer athlete mentor responses to the vignettes were analyzed using a dual narrative analysis. Peer athlete mentors responded to the least hesitant vignettes by drawing on mentee narratives rather than privileging their own view of sport and SCI. As such, peer athlete mentors provided individualized sport recommendations rather than a generic list of options for individuals. For the most resistant vignette, peer athlete mentors expressed one of two responses: one that challenged the mentees’ disability narrative and one that allowed mentees to express their own story of disability. Given the difficulty peer athlete mentors had in forming a connection with the individual who expressed a heavily resistant narrative, training for peer athlete mentors should address these possible narratives along with practice in how to respond to individuals with different disability narratives.Acknowledgments: This research was supported by a Joseph-Armand Bombardier CGS Doctoral Scholarship (SSHRC).
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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 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".