« Figure it out on your own »: a mixed-method study on pelvic health survivorship care after gynecologic cancer treatments
Bibliographic record
Abstract
Abstract Purpose. Pelvic health issues after treatment for gynecological cancer are common. Due to challenges in accessing physiotherapy services, exploring virtual pelvic healthcare is essential. This study aims to understand needs, preferences, barriers, and facilitators for a virtual pelvic healthcare program for gynecological cancer survivors. Methods. A multi-center, sequential mixed-methods study was conducted. An anonymous online survey (N=50) gathered quantitative data on pelvic health knowledge, opportunities, and motivation. Focus groups (N=14) explored patient experiences and consensus on pelvic health interventions and virtual delivery. Quantitative data used descriptive statistics and focus group analyses employed inductive thematic analysis. Findings were mapped to the Capability, Opportunity and Motivation (COM-B) behavior change model. Results. Participants reported lacking knowledge about pelvic health interventions and capability related to the use of vaginal dilators and continence care. Barriers to opportunity included lack of healthcare provider initiated pelvic health discussions, limited time in clinic with healthcare providers, finding reliable information, and cost of physical therapy pelvic health services. Virtual delivery was seen favorably and may help to address motivational barriers related to embarrassment and frustration with care. Conclusion. Awareness of pelvic healthcare is lacking among people treated for gynecological cancer. Virtual delivery of pelvic health interventions is perceived as a solution to enhance access while minimizing travel, cost, embarrassment, and exposure risks. Implications for cancer survivors. A better understanding of the pelvic health needs of individuals following gynecological cancer treatments enables the development of tailored virtual pelvic floor rehabilitation interventions which may improve access to pelvic health survivorship care.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".