Key Learnings and Considerations for Utilizing Social Media Recruitment in Parasport Research
Bibliographic record
Abstract
Despite the rise of digital methodologies in qualitative health and sports research (Goodyear & Bundon, 2020), there remains a gap in the usage of online methods in parasport populations. People with disabilities are often underrepresented in research as they are a traditionally hard-to-reach population due to accessibility limitations, stigmatization, and mistrust of researchers (Banas et al., 2019). The existing role of social media as a space for advocacy and social support in the parasport community (Bundon & Clarke, 2014) makes social platforms a potential tool for qualitative parasport research. Project Echo looks to leverage social media as a participant recruitment tool in parasport populations by informing recruitment strategies with the social model of disability and by placing an emphasis on collaboration to avoid the historical medicalization and marginalization of participants with disabilities in research. This article draws on the experiences of social media recruitment from Project Echo and aims to inform researchers looking to utilize social media as a research tool in parasport populations with key learnings and considerations. Banas, J. R., Magasi, S., The, K., & Victorson, D. E. (2019). Recruiting and Retaining People With Disabilities for Qualitative Health Research: Challenges and Solutions. Qualitative Health Research, 29(7), 1056–1064. https://doi.org/10.1177/1049732319833361 Bundon, A., & Hurd Clarke, L. (2014). Unless you go online you are on your own: Blogging as a bridge in para-sport. Disability & Society, 30(2), 185–198. https://doi.org/10.1080/09687599.2014.973477 Goodyear, V., & Bundon, A. (2020). Contemporary digital qualitative research in sport, exercise and health: Introduction. Qualitative Research in Sport, Exercise and Health, 13, 1–10. https://doi.org/10.1080/2159676X.2020.1854836
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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 | 0.426 | 0.507 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.032 | 0.042 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.013 | 0.017 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".