Rehabilitation providers’ experiences with long COVID care in Canada: a qualitative study
Bibliographic record
Abstract
To examine the experiences, challenges, and recommendations of Canadian Rehabilitation Providers delivering care to people with Long COVID (PWLC), with the goal of informing best practices and guiding service and policy improvements. A qualitative descriptive study was conducted using semi-structured interviews with 32 Rehabilitation Providers from multiple disciplines and provinces across Canada. Participants were purposively sampled and interviewed between April 2022 and January 2023. Data were analyzed using codebook thematic analysis, incorporating inter-coder and inter-rater agreement and reflexivity practices. Three central themes emerged: (1) Providers faced substantial barriers, including limited infrastructure, resource constraints, and misinformation about Long COVID; (2) In the absence of formal guidelines, providers adapted their practices through individualized care planning, trial-and-error, and peer learning; and (3) Participants emphasized key components of effective rehabilitation, such as validating patient experiences, promoting self-management and pacing, integrating caregiver support, facilitating peer connections, and fostering inter-professional collaboration. Rehabilitation Providers have been critical in addressing the evolving needs of PWLC despite inadequate systemic support. Their insights point to the need for coordinated, interdisciplinary, and patient-centered care models, alongside investment in professional training, system infrastructure, and integrated policy responses for current and future post-viral conditions. Rehabilitation professionals are recommending individualized and flexible care approaches focused on symptom validation and energy conservation, due to the diverse and unpredictable nature of Long COVID.In the absence of formal guidelines, rehabilitation professionals have adapted practices in real time, emphasizing the urgent need for standardized, evidence-informed Long COVID rehabilitation frameworksEffective Long COVID rehabilitation includes patient and caregiver education, integration of peer support, and the promotion of self-management strategies to enhance recovery and reduce isolation.Interdisciplinary collaboration and system-level integration are critical to ensure continuity of care and to build sustainable rehabilitation pathways for emerging chronic conditions like Long COVID. Rehabilitation professionals are recommending individualized and flexible care approaches focused on symptom validation and energy conservation, due to the diverse and unpredictable nature of Long COVID. In the absence of formal guidelines, rehabilitation professionals have adapted practices in real time, emphasizing the urgent need for standardized, evidence-informed Long COVID rehabilitation frameworks Effective Long COVID rehabilitation includes patient and caregiver education, integration of peer support, and the promotion of self-management strategies to enhance recovery and reduce isolation. Interdisciplinary collaboration and system-level integration are critical to ensure continuity of care and to build sustainable rehabilitation pathways for emerging chronic conditions like Long COVID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".