Rehabilitation Capacity for Long COVID Care in British Columbia
Bibliographic record
Abstract
Introduction: Many Long COVID symptoms impact daily life and are amenable to rehabilitation. Assessing the rehabilitation system's capacity to care for Long COVID patients is crucial to support care planning. Aim: Explore the capacity of physiotherapists (PTs) in British Columbia (BC), Canada, to provide rehabilitation for adults with Long COVID. Methods: An online survey was distributed to BC PTs, including quantitative and qualitative questions on their skills, confidence, and capacity to care for Long COVID patients. Descriptive statistics and content analysis were used. Results: Of 2,281 contacted, 139 PTs responded. Most were women (72%) with a mean of 10 (0–47) years of clinical practice. Participants were geographically well-distributed across the province. Fewer than half had treated Long COVID patients, providing a mean of 10 (3–50) sessions. PTs mainly addressed orthopedic issues (e.g., muscle weakness, balance issues) but had limited experience with prevalent Long COVID symptoms like dyspnea, fatigue, and chest pain. Two-thirds lacked confidence in assessing and treating Long COVID. PTs expressed a need for further training in the assessment and treatment of cardiorespiratory conditions, cognitive and mental health issues, and energy management/fatigue. Nearly half reported having the capacity to accommodate Long COVID patients within a few weeks but highlighted challenges, including funding, staff shortages/burnout, lack of standardized care pathways, equity and access issues, and the need for specialized training. Conclusion: Physiotherapy services in BC have reduced capacity to support Long COVID patients, largely due to PTs’ limited experience and expertise in managing Long COVID-specific symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".