Referral Patterns And Barriers To Physical Rehabilitation For Children And Adolescents With Cancer Across Canada
Bibliographic record
Abstract
Two thirds of children who have been diagnosed with cancer will develop at least one chronic or long-term adverse effect of cancer treatment, many of which are amenable to physical rehabilitation (PR). PR may help reduce the burden of cancer side effects; however, research suggests few childhood cancer survivors access PR services. PURPOSE: To explore PR referral patterns and barriers to service provision for children and adolescents with cancer across Canada. METHODS: A cross-sectional web-based survey in English and French languages was conducted. Participants identified were Canadian healthcare professionals (HCPs) who provide and/or refer children and adolescents with cancer to PR services. The survey gathered data on numbers of childhood cancers either seen or referred to PR, reasons that prompted referral to PR, and existing barriers and facilitators to PR programs. RESULTS: A total of 54 responses were received including physical therapists (n= 27), nurse and nurse practitioners (n= 10), pediatric oncologists and oncology residents (n= 9), occupational therapists (n= 6), a speech-language pathologist (n= 1), and an exercise professional (n= 1). Data indicate that approximately 25% of children with cancer are being referred to PR, suggesting less than optimal referral rates. While 70% of HCPs report referring children and adolescents to PR services; the primary reason for referral is when the child presents with, or is at risk of functional disability. Chemotherapy-induced peripheral neuropathy (CIPN) was the second most common reason for referral to PR services (63%), and was identified by survey respondents as a rehabilitation research priority. The existence of a multidisciplinary team (52%), as well as the availability of PR space and equipment (33%) were the most commonly reported facilitators by the oncology medical team; while barriers to service provision included a lack of staffing (24%) and specialized PR services (17%). CONCLUSIONS: The survey results demonstrate that gaps in the health system such as limited resources and specialized services exist, that impact the implementation of PR programs. Results from this survey have informed the design of upcoming research investigating the feasibility and effects of an early PR program in children at risk of developing CIPN. No funding was received.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".