Perspectives of Rehabilitation Professionals on Long COVID Interventions to Facilitate Return-to-Work
Bibliographic record
Abstract
Background. The severe functional impact of long COVID presents a significant challenge for clients seeking to return to work. Despite emerging clinical management guidelines, long COVID remains a concern in the rehabilitation field. There is a need to establish optimal practices for sustainable rehabilitation paths that enhance the recovery of clients with long COVID, all while understanding the challenges faced by rehabilitation professionals working with this population. Purpose. This study aimed to explore the perspectives of rehabilitation professionals intervening in long COVID rehabilitation with the goal of returning to work. Methods. A qualitative study was conducted involving online semi-structured interviews with rehabilitation professionals in Quebec from public and private sectors across various regions who had experience treating individuals with long COVID. Thematic analysis was employed for data analysis. Findings. Nine rehabilitation professionals participated in the study, yielding five themes: (a) reassessment of RTW goals; (b) education and self-management as primary interventions; (c) gradually reintegrating daily activities and life habits; (d) progression of interventions and dealing with post-exertional malaise (PEM); and (e) challenges in long COVID rehabilitation. Conclusion. Education, gradual activation and self-management appear as central components in supporting patient recovery, however, achieving return to work remains challenging without proper accommodations.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".