Exploring the Factors Involved in Being “Ready” to Return to Sport Following a Concussion
Bibliographic record
Abstract
OBJECTIVE: To explore the factors involved in athletes being ready (or not) to return to sport (RTS) after sport-related concussion (SRC). DESIGN: Qualitative, semistructured interviews.Setting : Videoconference.Participants : Twenty-two sport-injury stakeholders involved in contact and collision sports at various levels of competition (high school, university, professional), including: formerly concussed athletes (n = 4), coaches (n = 5), athletic therapists (n = 5), physiotherapists (n = 4), nurse practitioner (n = 1), and sports medicine physicians (n = 3). INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: We included questions in the interview guide regarding factors participants believed were involved in athletes being ready (or not ready) to RTS after a concussion. RESULTS: Participants described physical (concussion symptoms, return to pre-injury fitness), behavioral (changes in behavior, avoidance, malingering), psychological (individual factors, cognitive appraisals, mental health), and social (isolation, social support, communication, pressure) factors that they believed were involved in athletes being ready to RTS after SRC. CONCLUSIONS: The graduated RTS strategy outlined in the most recent Concussion in Sport Group consensus statement focuses on physical aspects involved in being ready to RTS, which does not address behavioral, psychological, and social factors, which were identified by participants as being related to returning to sport post-SRC. More research is needed to determine whether the additional factors outlined in this study are relevant among larger samples of athletes, coaches, and healthcare professionals.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".