Enhancing Pelvic Health: Optimizing the Services Provided by Primary Health Care Teams in Ontario by Integrating Physiotherapists
Bibliographic record
Abstract
Purpose: The purpose of this review was threefold: (1) to outline the current landscape of service provision for two common pelvic floor disorders, urinary incontinence (UI) and pelvic organ prolapse (POP); (2) to describe common pelvic floor dysfunctions (UI and POP) and the associated evidence-based, conservative care; and (3) to present the potential to integrate physiotherapists into inter-professional primary health care teams to optimize the provision of care for these disorders. Method: A literature review was undertaken and a case study was developed to describe evidence-informed conservative care for pelvic floor dysfunctions. Results: A variety of models exist to treat pelvic floor disorders. Physiotherapists and nurses are key care providers, and their scope and care provision overlaps. In Ontario specifically, both nurses and physiotherapists with additional postgraduate training in pelvic floor disorders are integrated into primary health care, but only to a very limited degree, and they are arguably well positioned to leverage their skills in their respective scopes of practice to optimize the provision of pelvic health care. Conclusions: Physiotherapists and nurses are shown to be key providers of effective, conservative care to promote pelvic health. There is an opportunity to integrate these types of provider into primary care organizations in Ontario; this collaborative care could translate into improved outcomes for patients and the health care system at large.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".