Social Support and Return to Sport: A Prospective Explanatory-Sequential Mixed Methods Study of Concussed University Athletes
Bibliographic record
Abstract
The Return-to-Sport (RTS) protocol is recommended for rehabilitating concussed athletes (Patricios et al., 2023). Limited studies exist on psychosocial factors influencing RTS (Bloom et al., 2022). Social support is a psychosocial factor that influences concussion rehabilitation (Kita et al., 2020). We explored nine (n = 7 females, n = 2 males) Canadian university rugby, basketball, and track and field athletes’ (Mage = 21.00, SD = 1.80) social support during RTS through a prospective mixed-methods design. Support agents were identified and ranked using concentric circles maps (Van Waes & Van den Bossche, 2019), the types and degree of support were measured with the Perceived Available Support in Sport Questionnaire (PASS-Q; Freeman et al., 2011) and experiences were explored through two semi-structured interviews. Descriptive and frequency-based analyses were performed on concentric circles maps and PASS-Q data. Codebook thematic analyses were performed on interview data (Braun & Clarke, 2021). Athletes identified 16 agents. Athletic therapists, student trainers, head coaches, significant others, and teammates/friends were most important. Significant others provided the most emotional (M = 2.82, SD = 1.85) and esteem (M = 2.63, SD = 1.85) support. Athletic therapists (M = 1.86, SD = 1.35) and head coaches (M = 0.93, SD = 1.19) provided the most informational and tangible support. Interviews showed support behaviours (e.g., checking-in), contextual factors (e.g., team-norms), concussion and RTS factors (e.g., uncertain timeline), and factors influencing psychological readiness (e.g., fear) impacted athletes’ RTS. Together, results demonstrate social support fluctuates throughout concussion RTS by agent, type, and behaviour.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".