A Framework for the Development and Implementation of an Advanced Practice Role for Physiotherapists That Improves Access and Quality of Care for Patients
Bibliographic record
Abstract
A new model of care has been implemented at the Sunnybrook Holland Orthopaedic and Arthritic Centre that expands the role of physiotherapists to improve access and quality of care for patients requiring hip and knee replacement surgery. An advanced practice physiotherapist (APP) role was created to support both referral management and post-operative care to reduce surgeon workload and better streamline services. This article describes our nine-step framework for implementing an APP role and can be used as a template for other organizations evolving similar roles. The framework was adapted from the participatory, evidence-based, patient-focused process for the development of an advanced practice nurse role. Key steps include (1) obtaining stakeholder consensus, (2) identifying barriers and facilitators and (3) developing the necessary administrative and training supports as well as clinical protocols and an evaluation framework. Approaching change in a series of small steps (plan-do-study-act [PDSA] methodology) alongside existing processes has facilitated buy-in and role acceptance. The early and continued involvement of decision-makers within the organization has been paramount to successful implementation. In addition, patient input has been central to the evolution of the role, with patient satisfaction a key indicator. The new role and model of care reconfigures traditional roles and introduces a team approach that results in timely access to care for patients. Benefits include an improved assessment process, enhanced education across the care continuum and improved coordination and delivery of services.
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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 | 0.089 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".