Requirements and Value Elicitation for a High-Fidelity Pelvic Floor Simulator for Physiotherapists: Mixed Methods Study
Bibliographic record
Abstract
Background: Physiotherapists lack training opportunities for repeated practice of pelvic examinations for the identification of pelvic floor disorders (PFDs), leading to low confidence in the clinical setting. Pelvic simulators exist and are a valuable supplement to the medical curriculum, yet none demonstrate pelvic floor muscle (PFM) function or dysfunction. To design effective simulators, an assessment of end-user requirements is essential. Objective: This study aimed to elicit physiotherapists' needs and requirements for a high-fidelity PFM simulator and the associated use cases. Methods: This study followed a mixed methods design by collecting qualitative and quantitative data from a web-based survey. Quantitative data were analyzed using descriptive statistics and differences between demographic groups were calculated using 2-sample Kolmogorov-Smirnov 2-sided tests. Qualitative data were analyzed using thematic analysis. Results: In total, 66 physiotherapists completed the survey. The most common suggested use cases of the simulator were for training and professional development (56/66, 84.9%), and patient education (48/66, 72.7%). Pelvic organ prolapse and muscle tone function and dysfunction were identified as the most useful PFDs for the simulator to demonstrate. Positional tracking and force sensing were considered important features and there was a preference for a generic over a pathology-specific or patient-specific simulator. A total of 3 themes emerged through the qualitative analysis: prioritizing patient care; representing the variability in anatomy and PFDs for simulator realism; and consideration of the implementation, cost, and accessibility of simulators. Conclusions: There is value in PFM simulators for physiotherapists for multiple use cases. Design recommendations include using realistic materials, demonstrating PFM dynamics, modularity to vary the complexity for different end-users, offering a range of feedback modalities for position and pressure sensing, and ensuring affordability and curriculum integration.
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 | 0.076 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".