Knowledge, Attitudes, and Practice of Pelvic Floor Muscle Training in People With Spinal Cord Injury: A Cross-Sectional Survey
Bibliographic record
Abstract
Background There is emerging evidence that pelvic floor muscle training (PFMT) may be useful for treating some urogenital conditions in people with spinal cord injury (SCI). Future clinical investigations would benefit from understanding the extent to which people with SCI are aware of and practicing PFMT, and their attitude toward this therapy. Objective The goal of this study was to assess the knowledge, attitudes, and practices related to PFMT among people with SCI. Methods We distributed an internet survey internationallyviaSCI related organizations for 2 months. We used descriptive statistics to summarize each survey item, and Chi-square and Mann-Whitney U tests to explore the differences in results between sexes and level of motor-function. Results Complete data from 153 respondents were analyzed. Sixty-two percent of respondents were female and 71% reported having complete paralysis. More than half of respondents reported being aware of PFMT (63%); more females than males reported knowledge of PFMT (p= 0.010). Females (p= 0.052) and people with partial paralysis (p= 0.008) reported a stronger belief that they would benefit from PFMT. Few people with SCI had practiced PFMT (20%), and of those who practiced, most of them had SCI resulting in partial paralysis (p= 0.023). Conclusions While people with SCI may be aware of and have favorable attitudes toward PFMT, few had practiced PFMT and there were notable differences in attitudes toward PFMT depending on the sex and level of motor function of the respondents.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".