The Use of Physiotherapy Navigators in Acute Cancer Care Settings: A Scoping Review
Bibliographic record
Abstract
Objective: The objective of this review was to explore and map the use of physiotherapist (PT) navigator roles, their settings, decision-making processes, interventions delivered, barriers, and facilitators in acute cancer care settings. Data Sources: Five databases and gray literature sources were searched to July 4, 2024. Study Selection: To be included in this review, studies must have included adults ≥18 years old living with cancer, used a navigation role or model of care led by a physiotherapist, and occurred in an acute cancer care setting (ie, a setting administering cancer treatments to individuals with a current diagnosis of cancer). Data Extraction: Two independent reviewers completed all screening and data extraction. Data Synthesis: Data were summarized narratively, and descriptive statistics were provided where applicable. Thirteen references were included; 6 perspective papers, 5 research articles, and 2 presentations. The characteristics of PT navigator roles varied but mainly included triaging through assessment (n=13; 100%), exercise planning and prescription (n=9; 69.2%), referral to appropriate services (n=7; 53.8%), barrier identification (n=5; 38.5%), providing education (n=4; 30.8%), and goal setting (n=2; 15.4%). Most PT navigators (n=10; 76.9%) interacted with patients within the first month of treatment and followed up at various timepoints (eg, weekly, monthly, as needed). A range of facilitators (eg, onsite services, support from the medical team, no patient cost to interact with the PT navigator) and barriers (eg, lack of health system funding, lack of medical team knowledge of rehabilitation, and additional patient costs) to the PT navigator role were identified. Conclusions: This review summarized and mapped the current evidence regarding PT navigation in acute cancer care settings. Future research and clinical programs to enhance the design and implementation of such roles are recommended.
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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 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".