Exploring the interactions of athletes and their social support network following sport-related concussion
Bibliographic record
Abstract
Research on social support following sport-related concussion (SRC) has largely been examined from the athlete perspective. This qualitative study explored social support interactions during recovery following SRC. We conducted semi-structured interviews with six athletes and 16 individuals who were identified as being part of the athletes' social support network (e.g., teammates, friends, or family members). All 22 participants in this study completed a timeline mapping activity, which allowed participants to share details about the athletes' SRC recovery, including the type and timing of support provided and received. Using thematic analysis, we found three themes. First, we found that social support was optimal when perceptions of social support were aligned (e.g., delivery and perceived impact on recovery). Second, we found several instances where challenges arose in the social support relationships, often stemming from incongruent perspectives (e.g., expectations and perceptions of support differed). Third, members of the support network described some of the barriers they faced when attempting to provide social support to athletes. Overall, these results add to the literature by demonstrating the good (aligned perspectives), the bad (incoherent perspectives), and the challenges with the social support relationships following SRC from the perspective of athletes and members of their support network.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".