'You’re the best liar in the world’: a grounded theory study of rowing athletes’ experience of low back pain
Bibliographic record
Abstract
OBJECTIVES: Low back pain (LBP) is common in rowers and leads to considerable disability and even retirement. The athlete voice can help clinicians to better understand sport-related pain disorders. We aimed to capture the lived experience of LBP in rowers. METHODS: Cross-sectional qualitative study using a grounded theory approach. Adult competitive rowers with a rowing-related LBP history were recruited in Australia and Ireland. Data were collected through interviews that explored: context around the time of onset of their LBP and their subsequent journey, experiences of management/treatment, perspectives around present beliefs, fears, barriers and expectations for the future. RESULTS: The 25 rowers (12 women/13 men) who participated were aged 18-50 years; they had a mean 12.1 years of rowing experience. They discussed a culture of concealment of pain from coaches and teammates, and fear of being judged as 'weak' because of the limitations caused by LBP. They reported fear and isolation as a result of their pain. They felt that the culture within rowing supported this. They reported inconsistent messages regarding management from medical staff. Some rowers reported being in a system where openness was encouraged-they regarded this a leading to better outcomes and influencing their LBP experience. CONCLUSIONS: Rowers' lived experience of LBP was influenced by a pervasive culture of secrecy around symptoms. Rowers and support staff should be educated regarding the benefits of early disclosure and rowers should be supported to do so without judgement.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".