Health professionals’ and patients’ perspectives on pelvic floor muscle training adherence—<i>2011 ICS State‐of‐the‐Science Seminar research paper IV of IV</i>
Bibliographic record
Abstract
AIMS: There is scant information on pelvic floor muscle training (PFMT) adherence barriers and facilitators. A web-based survey was conducted (1) to investigate whether responses from health professionals and the public broadly reflected findings in the literature, (2) if responses differed between the two groups, and (3) to identify new research directions. METHODS: Health professional and public surveys were posted on the ICS website. PFMT adherence barriers and facilitators were divided into four categories: physical/condition, patient, therapy, and social-economic. Responses were analyzed using descriptive statistics from quantitative data and thematic data analysis for qualitative data. RESULTS: Five hundred and fifteen health professionals and 51 public respondents participated. Both cohorts felt "patient-related factors" constituted the most important adherence barrier, but differed in their rankings of short- and long-term barriers. Health professionals rated "patient-related" and the public "therapy-related" factors as the most important adherence facilitator. Both ranked "perception of PFMT benefit" as the most important long-term facilitator. Contrary to published findings, symptom severity was not ranked highly. Neither cohort felt the barriers nor facilitators differed according to PFM condition (urinary/faecal incontinence, pelvic organ prolapse, pelvic pain); however, a large number of health professionals felt differences existed across age, gender, and ethnicity. Half of respondents in both cohorts felt research barriers and facilitators differed from those in clinical practice. CONCLUSIONS: An emphasis on "patient-related" factors, ahead of "condition-specific" and "therapy-related," affecting PFMT adherence barriers was evident. Health professionals need to be aware of the importance of long-term patient perception of PFMT benefits and consider enabling strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".