Essential Topics to Include in a Pelvic Floor Workshop for Physiotherapists
Bibliographic record
Abstract
Pelvic floor dysfunction (PFD) is prevalent among women and can greatly impact quality of life. Pelvic floor physiotherapy (PFPT) has been shown to be the most effective treatment method for PFD, however, pelvic floor education is highly variable across physiotherapy (PT) programs. This study aims to develop and assess the effectiveness of a PFPT workshop for community PTs (CPT) with no formal training in pelvic floor health, which includes content deemed essential by PT experts (PTE) in the field. PTEs were asked to rank a variety of anatomical and PFPT topics. Those considered “essential” by the majority were included in the workshop. An online workshop was developed for CPTs based on these rankings, which included four asynchronous anatomy modules and one synchronous case‐based session with two PTEs. The CPTs will be assessed for their knowledge of the essential topics prior to and following the workshop. Essential anatomy topics included not only the pelvic organs but also other musculature related to pelvic floor function, including those in the gluteal region, deep back muscles, and the diaphragm. Essential PFPT topics include assessment of pelvic floor muscle function and pain, diagnosing PFD based on clinical presentation, and treatment options for PFD such as pelvic floor muscle training and relaxation training. A PFPT workshop should not be limited to the anatomy of the pelvic floor and PFPT and should include the related anatomy that may be impacted in PFD. It is expected that following the workshop, CPTs will have a better understanding of PFD and its clinical presentations as it does not always clearly present in clients as pelvic floor issues.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".