Disclosure of possible concussions in National Rugby League Women's Premiership players
Bibliographic record
Abstract
OBJECTIVES: This study investigated the disclosure and reasons for non-disclosure of possible concussions and their symptoms in National Rugby League Women's (NRLW) Premiership players in Australia. DESIGN: Cross sectional survey. METHODS: During the 2022 NRLW season, NRLW players were invited to participate in a voluntary, anonymous, online survey exploring (i) player demographics, (ii) rugby playing history, (iii) concussion disclosure, and (iv) instances of, and reasons for, non-disclosure of possible concussions to medical staff during the past two seasons. Logistic regression analyses were used to identify reasons for non-disclosure of possible concussions in NRLW players. RESULTS: Of the 132 eligible participants, 86 players responded to the survey and 63 % (n = 54/86) reported that they always disclosed a possible concussion during the past two seasons. A substantial number of NRLW players surveyed (n = 32/86, 37 %) did not disclose a possible concussion to their team or medical staff on one or more occasions. Sixty-three players (73 %) always reported symptoms during a medical assessment. Twenty-three players (27 %) did not disclose their symptoms during a medical assessment, primarily during or after a game or training session (n = 12/23, 52 %). Of the players who did not disclose their possible concussion symptoms, the two main reasons for non-disclosure were 'not wanting to be ruled out of the game or training session' (n = 8/23,35 %) and not being 'sure if the symptoms were related to concussion' (n = 8/23, 35 %). Most surveyed players (n = 74/86, 86 %) reported attending mandatory concussion education sessions at their respective clubs. CONCLUSION: We found high rates of non-disclosure amongst NRLW players, which is inconsistent with previous research suggesting that women are more aware of their symptoms than men and more likely to disclose their concussions. Not wanting to be ruled out of the game or training session and being unsure if the symptoms were related to concussion were the two most common reasons for nondisclosure. Concussion education initiatives could promote a supportive culture fostering disclosure amongst all stakeholders to ensure optimal player welfare.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".