Physiological and nutrition-related challenges as perceived by spinal cord-injured endurance hand cyclists
Bibliographic record
Abstract
This study explored the perceptions of spinal cord-injured (SCI) endurance hand cyclists regarding their physiological and nutrition-related challenges and the perceived impact of these challenges on nutritional intake and exercise capacity. This was an interpretive qualitative descriptive study in which semi-structured interviews were conducted with 12 adult South African national-level SCI endurance hand cyclists. Thematic analysis was used to explore perceptions regarding physiological and nutrition-related challenges and the impact thereof on nutritional practices and exercise capacity. Four themes emerged from the interviews: (i) physiological challenges experienced, (ii) nutrition-related challenges experienced, (iii) changes in nutritional practices, and (iv) compromised exercise capacity. The SCI endurance hand cyclists reported a number of physiological and nutrition-related challenges. Bowel and bladder challenges, limited hand function, muscle spasms, thermoregulatory challenges, pressure sores, menstrual periods, and low iron levels/anaemia were perceived to predominantly impact food and fluid intake (restrict intake) and compromise exercise capacity. This information can assist to devise tailored guidelines aimed to optimise fluid intake, overcome bladder challenges and ensure adequate nutritional intake in light of limited hand function.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".