“There's definitely something wrong but we just don't know what it is”: A qualitative study exploring rowers' understanding of low back pain
Bibliographic record
Abstract
OBJECTIVES: Low back pain is highly prevalent in rowing and can be associated with significant disability and premature retirement. A previous qualitative study in rowers revealed a culture of concealment of pain and injury due to fear of judgement by coaches or teammates. The aim of this study was to explore rowers' perspectives in relation to diagnosis, contributory factors, and management of low back pain. DESIGN: Qualitative secondary analysis. METHODS: We conducted a secondary analysis of interview data previously collected from 25 rowers (12 in Australia and 13 in Ireland). A reflexive thematic analysis approach was used. RESULTS: We identified three themes: 1) Rowers attribute low back pain to structural/physical factors. Most rowers referred to structural pathologies or physical impairments when asked about their diagnosis. Some participants were reassured if imaging results helped to explain their pain, but others were frustrated if findings on imaging did not correlate with their symptoms. 2) Rowing is viewed as a risky sport for low back pain. Risk factors proposed by the rowers were primarily physical and included ergometer training, individual technique, and repetitive loading. 3) Rowers focus on physical strategies for the management and prevention of low back pain. In particular, rowers considered stretching and core-strengthening exercise to be important components of treatment. CONCLUSIONS: Rowers' understanding of low back pain was predominantly biomedical and focused on physical impairments. Further education of rowers, coaches and healthcare professionals in relation to the contribution of psychosocial factors may be helpful for rowers experiencing low back pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".