Understanding Social Support Throughout the Injury Process among Interuniversity Swimmers
Bibliographic record
Abstract
The purpose of this study was to gain comprehensive understanding of athletes’ social support experiences during the injury process, with a focus on social support networks, exchanges, and appraisals (Bianco & Eklund, 2001). Twelve university swimmers who recently experienced swimming-related injuries engaged in a semistructured interview. Findings indicate athletes had mixed experiences with their networks of social support (i.e., coaches, medical practitioners, parents, and teammates), with themes regarding exchanges and appraisals emerging in three categories: (a) Don’t bring your negative energy to practice, (b) Show me you care, and (c) Provide me with some clear and appropriate direction! Participants reported coaches and teammates being in denial of their injuries, shunning them from the team, or pushing them to train through their injuries, resulting in athletes feeling uncared for, unsupported, and lacking direction. Athletes’ sense of support stemmed from feeling cared for. Findings underscore the importance of comprehensively examining the multiple constructs of social support, while serving as a springboard for further investigations and important practical implications.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".