Where best practice pain care and patient expectations for care meet: Exploring patient expectations around chronic pelvic pain, physiotherapy, and the biopsychosocial model of care
Bibliographic record
Abstract
BACKGROUND: Pelvic physiotherapy (PT) is a holistic and evidence-based treatment for chronic pelvic pain (CPP). It is important to understand patient expectations for treatment to improve patient satisfaction and outcomes. The current literature lacks information about patient expectations for CPP pelvic PT. OBJECTIVES: To describe the expectations around treatment and the role of pelvic PT for patients with CPP. DESIGN: We conducted a qualitative study and interviewed 10 participants who were on the waitlist for CPP PT at Women's College Hospital in Toronto, ON, about CPP and pelvic PT. METHODS: We recruited patients on the pelvic pain PT waitlist who were assigned female at birth, 18 years of age or older, diagnosed with CPP for more than 6 months. The 1 -h long interviews were conducted via Zoom by two PT students before being transcribed with NVivo. Inductive content analysis was used to create themes and categorize the participant data. RESULTS: We describe three main themes to convey the experiences of participants living with CPP and their expectations for pelvic PT: (1) Expectations are clouded by a lack of understanding, (2) Pelvic PT will provide a new way to get relief, and (3) My role is to be open to try new things. CONCLUSIONS: Pelvic PT should incorporate education regarding CPP, strong therapeutic alliance with the patient, effective communication, and integration of the biopsychosocial approach to care to better meet patient expectations and improve quality of care. This study highlights the critical importance of providing patients with consistent, accurate, and comprehensive education on CPP, pain treatment and self-management strategies, and the role of pelvic PT. By delivering this foundational knowledge early in the patient's treatment plan, we can influence patient expectations, enhance both patient engagement and outcomes in pelvic PT, leading to a more holistic, informed, and effective approach to patient 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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".