Preferences, barriers and facilitators regarding virtual pelvic healthcare in individuals with gynaecological cancers: protocol for a patient-oriented, mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Vaginal pain during intercourse and urinary incontinence are common complaints after gynaecological cancer treatments. Pelvic health physiotherapy treatments aim at optimising function through education on the use of vaginal moisturisers, dilation therapy programme and pelvic floor muscle training. Given that barriers such as time, travel, and costs are known to limit access to physiotherapy services, a virtual pelvic health physiotherapy programme may help to facilitate access. The primary objective of this study is to identify preferences, barriers and facilitators from individuals with gynaecological cancer regarding virtual pelvic healthcare survivorship care. METHODS AND ANALYSIS: This patient-oriented, mixed-methods study will involve an online cross-sectional survey data (phase I) and qualitative data from a series of virtual focus groups (phase II). PHASE I: an anonymous survey will be used to assess the demographics, health status, prevalence of urogenital symptoms, as well as knowledge, barriers and facilitators to pelvic health services of people with gynaecological cancer. A total of N=50 participants from Canada will be recruited through convenience and self-selection sampling. PHASE II: a series of virtual semi-structured focus groups will be conducted with 10-15 participants on key topics related to virtual pelvic healthcare. Interviews will be audio-recorded and transcribed, from which key themes and quotes will be identified. An interpretive description qualitative method will guide analysis and implementation of results. ETHICS AND DISSEMINATION: Approval from the Health Research Ethics Board of Alberta-Cancer Committee (HREBA.CC-21-0498) and of the CISSS Bas-Saint-Laurent (CISSSBSL-2021-10) have been obtained. Informed, electronically signed consent will be required from all participants. Results from this work will be published in a peer-reviewed journal and will be used to inform the development and implementation of a new Pelvic eHealth Module for individuals treated for gynaecological cancers. This module will be incorporated into a comprehensive educational and exercise programme offered by a web-based application.
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 | 0.059 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.012 |
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".